International technology spillovers from trade and FDI: a comparison between advanced and developing countries

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International technology spillovers from trade and

FDI: a comparison between advanced and developing

countries

University of Groningen

Faculty of Economics and Business

Research paper International Economics and Business

Name Student: Qian Chen

Student ID number: 2147769

Student email: nancyfly28@gmail.com

Date Paper: 17-01-2014

Supervisor: Dr. Sorin Krammer

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International technology spillovers from trade and

FDI: a comparison between advanced and developing

countries

Abstract:

This paper employs panel regression analysis to investigate the effect of

technology spillover via trade and FDI on productivity growth, and considers the role

of domestic institutional quality playing in affecting their relationship. A comparison

of the spillover effects is made between advanced and developing countries. My

findings show that trade spillovers originated from developing countries has a larger

influence on productivity than that from developed countries, and this effect is

enhanced by institutional quality. Besides, although FDI spillovers do not directly

promote productivity growth, it can also benefit productivity by complementing with

domestic institutional quality.

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1. Introduction:

It has long been recognized that technological change plays an important role in

enhancing economic growth, since it influence the ability of a country to muster and

efficiently use capital resources, accumulate human capital, and achieve technological

improvement through R&D, patent, etc (Lall, 1992). Among these three aspects,

following studies further indicates that a large share of variation in economic growth

is explained by improvement in technology, rather than development of human capital

and financial resources (Hall & Jones, 1999; Easterly & Levine, 2001).

According to Jan, Martin and Bart (2009), technology refers to knowledge about

physical processes as well as how to organize and manage these processes. Previous

studies usually use the difference in technological knowledge to explain productivity

differences across countries and industries. Advanced countries are the main engine of

creating knowledge, however, developing countries usually prefer to acquire them

from foreign countries, since innovation is costly, risky and path-dependent (Fu, et al.

2011), and developing countries have limited resources to invest in R&D and human capital.

Although technology and knowledge are tacit in nature, they could be transferred

across country through many channels, such as human mobility, patenting, licensing.

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only two main channels attract the most attention of researches: international trade

and foreign direct investment. As discussed by Costantini & Liberati (2011),

developed countries usually prefer FDI to overcome problems involved in transfer

tacit technology, which requires face-to-face contacts or procedural production

standards. Meanwhile, developing countries mainly regard international trade as a

vehicle of technology transfer from advanced countries. Therefore, the international

diffusion of technology plays a key role in explaining worldwide technological

change, which in turn narrowing the productivity gap between countries. For instance,

for some countries, foreign source of technology contributes to more than 90% of

domestic productivity growth (Keller, 2009).

It is important to notice that how much the recipient country can benefit from the

technology spillover effects of FDI and international trade depends on its institutional

quality. In this study, institutional quality refers to the level of development of formal

institutions in recipient countries, since decision making relative to international trade

and FDI are significantly affected by formal institutions (Jude & Levieuge, 2013;

Kucic, 2012) which are more powerful in guaranteeing exchanges than informal ones

(Luiz, 2009 .

As addressed by previous studies, institutional quality influences the ability of

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4 countries from 1984 to 2009, Jude and Levieuge (2013) find that institutional quality

do affect the spillover effects of FDI. Meanwhile, Bhattacharya et al. (2009) provide

the empirical evidence that institutional quality support the positive relationship

between international trade and economic development. However, these studies solely

focus on one channel of technology transfer. According to Krammer (2008), it is

important to regard FDI and trade as complements in the analysis of technology

spillover process.

This paper aims to conduct an empirical analysis towards the mechanism through

which R&D spillovers enhance economic growth, and considers international trade

and FDI as main channels of technology spillover. Since a large amount of studies has

proved that FDI and international trade promote productivity growth through

technology spillover effects, this paper compares the spillover effects between

developing and developed countries, because there is technology take-off in some

emerging economics, which already catch up with the developed countries (Fu, et al.

2008 and they may become new sources of innovation. Thus, the first research question of this paper is that does technology spillover from developed countries has

bigger positive influence on productivity development than that from developing

countries? In addition, with increasing importance of country-specific institution, this

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effects of trade and FDI simultaneously, which has not been addressed in previous

literature. In another word, does country-specific institutional quality affect

technology spillover effects of trade and FDI?

The rest of this paper proceeds as follows: section two presents the literature

review of relevant papers and sets hypotheses; section three provides the specification

of the theoretical model; section four introduces the data and methods applied; section

five discusses the empirical results; finally, the last section provides the conclusion,

limits of this paper as well as suggestions for future research.

2. Literature review

Technology spillover can be achieved when domestic firms learn about the

multinationals’ technology. This section briefly discusses some explanations of the technology spillover effects of international trade and FDI on productivity growth, as

well as the role of institutional quality in technology spillover process that is offered

by previous literatures. These researches also provide clues for the adopted indicators

in this study.

2.1 international trade, technology spillover and productivity growth

Theoretically, international trade flows are regarded as one important vehicle of

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6 process of international trade, firms in the recipient country get acquainted with the

characteristics of the imported goods, which ensure them to imitate or create a similar

technology. Secondly, international trade ensures the availability of increasing variety

of intermediate goods and capital equipments for domestic productions, which are

embodied in more advanced technologies in the production of final goods (Costantini

& Liberati, 2011; Bulent, 2008). Thirdly, trade is able to enhance transmission of

technical information, such as production processes, product design and managerial

methods, by increasing communication between domestic firms and multinationals

(Krammer, 2008).

As a result, these technologies transferred through international trade contribute

to productivity growth of recipient countries, which is supported by plenty of previous

studies. The initial empirical evidence relative to OECD countries is provided by Coe

and Helpman (1995), who demonstrate that there is a positive and significant effect of

technology spillover via international trade on total factor productivity. In their study,

the technology spillover via trade is measured by import-weighted foreign R&D.

However, their estimates are mainly focus on trade among developed countries. In

1997, a similar result is found by using a larger sample, which indicates that a 1%

increase in the foreign R&D stocks of industrialized countries increases output of the

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Furthermore, by using the same methodology as Coe and Helpman, Krammer

(2008 also find a positive and significant relationship between trade spillover and the total factor productivity among developed and transition countries. In addition, Lei

and Bang provide the empirical evidence that international trade remains an important

conduit for technology transfer, which is positively and significantly relative to

productivity growth.

However, the extent to which a country can benefit from international trade

depends on trade partners, who are able to exchange products and knowledge in

which the recipient country is in short supply (Coe, et al. 1997). The past decades

have witness impressively rapid economic growth in some developing countries, such

as Brazil, India and China. These countries have put rising emphasis on innovation on

different extents and with varying success (Fu, et al. 2010). But, there is no doubt that

developed countries still accounts for the largest share of R&D activities, and hold a

larger “stock of knowledge” than developing countries. Hence, developing countries seem to gain more by trading with developed countries, since advanced countries have

more knowledge and information desired by developing countries to acquire.

Based on above analyses, the first hypothesis which is to be tested in this paper

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8 𝐻₁: Technology spillover via international trade from developed countries has a bigger positive influence on productivity growth than that from developing countries.

2.2 FDI, technology spillover and productivity growth

An alternative and equally important channel for technology spillover is foreign

direct investment. Since governments across the world set more policies and spend

increasing amounts of resources to attract FDI, it is at least partly implies that the

production and research efforts undertaken by multinational affiliates within national

borders bring spillover benefits to domestic economy (Bransetetter, 2000).

Many studies provide some explanations about how technologies are transferred

through FDI. Initially, FDI requires establishment of international production

networks (Chang and Lee, 2009), which benefit domestic firms through backward and

forward linkages, since these linkages ensure local firms learn and imitate advanced

technologies as well as new production processes of foreign firms (Hermes and

Lensink, 2003). For instance, by taking the form of joint venture with Japanese firms,

South Korean firms are able to absorb foreign technology for a variety of consumer

goods and components, which contribute to their amazing functional upgrading of

electronic industry in the following years (Mike, 1995).

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provide domestic firms opportunities to benefit from labor turnover. The studies of

Song (2000) indicates that engineers who move from the US to Korea or Taiwan play

a significant role in the technological catch-up of Korea and Taiwan, since they

facilitate spillover of tacit knowledge.

In addition, in order to work with new technologies and deal with increasing

competition from foreign firms, domestic firms are more willing to train their

employees, use alternative management practices and better organizational

capabilities, as well as hire employees from foreign firms (Alfaro, et al., 2004; Chang

and Lee, 2009; Hermes and Lensink, 2003). These trends further facilitate the process

of technology spillover via FDI.

As a result, FDI spillover seems to enhance productivity growth. According to

Borensztein, et al. (1998), technological diffusion of FDI may lead to the expansion of

domestic firms. In other words, in the processes of technological diffusion, both

production and productivity of domestic firms would have a large improvement,

which presents a higher level of productivity in the recipient country.

A large amount of studies have investigated the effects of inward FDI on the

productivity growth of host counties. Although previous researches on the micro level

found either negative or no relationship between FDI and domestic productivity,

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10 which is calculated by FDI inflows-weighted foreign R&D, do promote productivity

growth. Besides, by analyzing the impact of US outward FDI on 40 host countries, Xu

(2000) finds that US multinationals contribute to the productivity growth in developed

countries. In addition, by using the FDI flows from G6 countries (the US, Japan, Italy,

the UK and Canada) to OECD countries plus Israel, Hejazi and Safarian (1999)

suggests that technology spillover has a positive relationship with productivity growth,

and this effect of FDI is larger than that of international trade.

However, FDI is not a homogenous capital flow, and some FDI projects are

better than others (Laura and Andrew, 2007), which result in different technology

spillover effects on the recipient countries. For instance, FDI inflows with better

knowledge involved will contribute to higher technology transfer to the recipient

countries (Jude & Levieuge, 2013). Since stock of knowledge in developed countries

is more advanced than in developing countries, it is reasonable to believe that FDI

spillover from developed countries can better contribute to productivity growth.

Hence, the second hypothesis of this paper is developed as follows:

𝐻2: Technology spillover via FDI from developed countries has a larger positive

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2.3 institutional quality and technology spillover

Although these two mechanisms of technology transfer may influence economic

performance positively, a country with a good environment is able to benefit more

from the diffusion process. One of crucial factor is the institutional quality condition.

As suggested by North (1990), institutions are the humanly designed constraints that

structure human interactions, which set market rules to organize interaction among

actors and bound economic actions in the market. These rules affect the nature of

competition and process of knowledge acquisition (Meyer & Sinani, 2009).

Institutions can be broadly classified into formal and informal ones. This paper

aims to investigate the role of institutional quality in the technology spillover- growth

relationship by focusing on formal institutions, which includes property rights, the

expropriate risk, and enforcement of law in a country. These factors in the host

country significantly shape the decision making of firms in the process of

international trade and FDI (Jude & Levieuge, 2013; Kucic, 2012). And according to

Luiz (2009), formal institutions are more powerful in guaranteeing exchanges than

informal ones, especially when societies become increasing complex and economies

are more sophisticated.

In the process of international trade, institutional quality influences transaction

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12 high transaction cost would exist in countries with bad institutions, since there is

increasing risk for long term trade commitments. As a result, high transaction cost

may decrease total interactions between multinationals and domestic firms, which

reduce the opportunities of technology spillover. Contrarily, well-functioned

institutional environment not only reduces transaction costs, but also decreases

information asymmetries, since good institutions are able to efficiently transfer

information to the market, which in turn enhance technology spillover.

More specifically, in order to benefit from international trade, the country must

reach a threshold level of institutional quality (Bhattacharya et al. 2009), because

formal institutions, such as the degree of control of property rights, the rules of laws,

increase gains from trade, which in turn boost development (Bhattacharya, 2009;

Costantini & Liberati, 2011). Costantini & Liberati (2011) provide the empirical

evidence, which indicate that institutions are a vehicle for facilitating technology

transfer through international trade. Therefore, the next hypothesis emphasizes on the

role of institutional quality in the relationship between trade spillover and productivity

growth.

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Institutional quality also impacts the extent of technology spillover via foreign

direct investment. As addressed by Ali (2008), propriety rights, the rule of law and

expropriation risk are the most significant institutional aspects relative to FDI. For

instance, unstable institutional environment with high level of uncertainty, low

protection of investor, inefficient law enforcement, can only attract resource-seeking

FDI with low-technology involved (Jude & Levieuge, 2013), because foreign firms

manage to avoid the risk of technology leakage, and protect their core competences.

Since these invest projects do not contains valuable technology, their contributions of

technology transfer are relatively lower than others.

Furthermore, in a less developed institutional environment where property rights

are poorly protected, foreign firms are exposes to expropriation risks (Kohler, 2010).

In this case, government of the host country may engage in nationalizing foreign firms

or favor local firms at the expense of foreign firms (Ali, 2008). Obviously, the

expropriate risk reduces incentives of foreign firms to invest in the target country.

With less foreign investment, local firms do not have much access to advanced

knowledge.

In addition, institutional quality influences the entry mode of FDI. Bad

institutional quality makes FDI to entry through mergers and acquisitions (Jude &

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14 relationship, acquired through mergers and acquisitions are useful for foreign firms to

substitute for the inadequacy of institutions. However, Using a sample of 84 countries

from1987 to 2001, Wang and Wong (2009) provide the empirical evidence that entry

by mergers and acquisitions has a less growth effect than green-field investment. As a

result, institutional quality influences the extent to which technologies can be

transferred from the home to host countries, which in turn moderates the relationship

between technology spillover and productivity growth.

Based on the analysis of FDI spillover, productivity growth and institutional

quality, the last hypothesizes are tested in this paper:

𝐻 Institutional quality alters the spillover effects of foreign direct investment on

productivity growth.

3. Empirical specification

The previous section has highlighted that technology could be transferred via

FDI and international trade, however, the growth effects of trade and FDI from

developed countries are different from that originated from developing countries.

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productivity growth from developed and developing countries. The basic model takes

the following form:

₁ ₂ (1

Where i presents the recipient country, t is the time index. is the logarithm of total factor productivity in the recipient country i in year t.

and are the technology spillover to country i via FDI originating from developing and developed countries, respectively. and represents the technology spillover to country i via international trade. is relative to spillovers in trade flows from developing countries, while refers to spillovers in trade flows from developed countries. is a vector of control variables, which includes domestic R&D intensity (RDI), human

capital (HC), and government expenditure (GE). is the error term.

Since there are no indicators for technology spillovers via FDI and trade, it is

computed following the method of Krammer (2008). In his analysis, the spillover

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16 recipient country as a percent of total FDI (trade) outflows from the originated

country to the world, times R&D intensity of the originated country. The total

technology spillover via FDI is shown in equation (2), while the total trade spillover is

shown in equation (3).

(∑ ) (2

Where t is the time index, i and q are the index of recipient and originated

country respectively. is the logarithm of total FDI spillovers from originated countries to the recipient country. presents the share of inward FDI

of country i originated from country q in year t as a percent of the total outward FDI

of country q in that year. indicates the R&D intensity of country q in the same

year.

(∑ ) (3

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country q in year t as a percent of the total export of country q in that year. Meanwhile,

are set as the same in equation (2).

In order to compare spillover effects between developing and developed

countries, this paper further develop Krammer’s method by computing spillover

variables originated from developing and developed countries respectively. The

calculation of FDI spillovers from developing countries is shown in equation (4).

∑ (4)

Where j is an index of developing countries. presents the share of inward FDI of country i originated from developing country j in year t as a percent of

the total outward FDI of country j in that year. indicates the R&D intensity of country j in the same year.

Besides, equation (5) specifies the calculation of trade spillover from developing

countries to recipient countries.

∑

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18 Where j is an index of developing countries. represents the share of import of recipient country i originated from developing country j in year t as a

percent of the total exports of country j in that year. Again, indicates the R&D intensity of country j in year t.

Meanwhile, this paper also interested in technology spillovers originated from

developed countries. The level of FDI spillover from developed countries is estimated

through equation (6).

∑ (6)

Where a is an index of developed countries. presents the share of inward FDI of the recipient country i originated from developed country a in year t as

a percent of the total outward FDI of country a in that year. indicates the R&D intensity of country a in the same year.

Furthermore, equation (7) estimates technological spillovers through trade from

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∑ (7)

Where a is the index of developed countries. is the share of imports of country i originated from developed country a in year t as a percent of the

total exports of country a in that year. indicates the R&D intensity of country a in year t.

In the above estimation, the coefficients ₁, ₂, , and are all expected to

be positive and significant. In order to test the hypothesis that technology spillover via

international trade (FDI) from developed countries has a bigger positive influence on

productivity growth than that from developing countries, this paper also expects:

1< 2 as well as < .

In order to investigate the role of domestic institutional quality in the process of

technology spillover, this paper employs two alternative models, which are presented

in equation (8) and equation (9). Equation (8) focuses on investigate the influence of

institutional quality on FDI spillover-productivity relationship. More specifically, an

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20 provide insights about whether institutional quality of recipient country is able to alter

the relationship between FDI spillover and productivity. Meanwhile, equation (9)

replaces the interaction term between FDI spillover variable and institutional quality

by an interaction term between trade spillover variable and institutional quality, and

aims to find whether institutional quality also affects the trade spillover-productivity

relationship. These two equations are developed as follows:

1 2 (8) 1 2 (9)

In both equation (8) and (9), represents the institutional quality of recipient country i. and the other variables are set as the same as in equations (1) –

(3).

In this estimation, if coefficients of the interaction term ₂ as well as ₂ are

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spillovers on productivity are larger for a higher level of domestic institutional quality,

which is stand in line with hypothesis 3a and hypothesis 3b.

4. Data and methods

This paper tends to involve a panel dataset of 37 countries in the period of 1990

to 2010. One reason this time period is chosen lies in that many developing countries’

R&D activities began to surge in the 1990s, thus it becomes interesting to observe

spillover effects originated from developing countries. In this dataset, 21 developed

countries and 16 developing countries are involved: 24 from Central and Eastern

Europe, 6 from Latin America, 5 from Asia. Meanwhile, the United States is from

North America, while New Zealand is from Oceania.

Refers to technology spillovers from developing countries, 16 developing

countries are set as the source for technology spillovers. Besides, when technology

spillover from developed countries is taken into consideration, 21 developed countries

are used for source of spillovers. In these two cases, both the developing and

developed countries in my sample are the recipient of these spillovers. In addition, for

equations (8) and (9) which investigate total technology spillover, each country is

considered as the recipient country, while the other 36 countries are the source of

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4.1 Dependent variable:

The dependent variable of this paper is the logarithm of total factor productivity

in the recipient country ( ), which is frequently used in previous studies (Coe

& Helpman, 1995; Coe, et al. 1997; Lei & Bang, 2007). Subject to a Cobb–Douglas

production function, the log of TFP is usually defined as the residual from the

aggregated output production function using the country’s stock of capital, labor force and output, as shown in equation (10).

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Where Y is the total output, K is capital stock, and L is total employment. The

parameters and represents the share of capital and labor in country i at year t. In this equation, the capital and labor shares are set as 0.35 and 0.65, which are

widely used in the literature and empirically validate (Krammer, 2008). The data on

GDP and employment comes from the Total Economy Database of the Conference

Board1. Besides, the data on capital stocks are cited from the World Bank’s World

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4.2 Independent variables

As discussed above, this study interests in the effects of four variables on

productivity growth: technology spillover of FDI from developing countries

( ), technology spillover of FDI from developed countries ( ),

trade spillover from developing countries ( ), as well as trade spillover from

developed countries ( ). Since the measurement of these variables is

explained in equations (4) – (7), I only introduce the data sources of these variables

here.

4.2.1 Technology spillover via FDI

Equation (4) and (6) are adopted to compute FDI spillovers for each country by

using following sources. Initially, this paper takes Bilateral FDI flows (US dollar,

millions) from the OECD International Direct Investment statistics3 for OECD

countries. However, the data do not cover most of the developing countries. As a

result, the UNCTAD World investment Directory4 and IMF’s Coordinated Direct

Investment Survey5 are adopted as a source of bilateral FDI data for developing

countries. In the case of China, the data are from National Bureau of statistics of

China6. Besides, the data of total outward FDI of each country comes from the

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24 UNCTAD Statistics7. In addition, the data of R&D intensity (gross R&D expenditure

as a percent of GDP) are cited from the World Bank’s World Development Indicators8,

supplemented by the Eurostat9 and national statistics.

However, not all of the bilateral FDI inflows are positive between partners. In

this case, this paper only allows for positive spillovers in country i from country j or

country , which is shown in formula (11) and (12). This is due to that if the value of bilateral FDI equals or less than zero, there is zero spillovers caused by disinvestment

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4.2.2 Technology spillover via trade

The calculation of trade spillovers is in a similar manner as the FDI spillovers,

which is based on equations (5) and (7). The data of bilateral trade (in US dollar) are

taken from IMF’s Direction of Trade Statistics10. Moreover, the data of total export

(in US dollar, millions) of all the countries comes from the UNCTAD Statistics11.

Before using of these data, they are transferred into the same unit.

4.2.3 Institutional quality

For the alternative models which focus on the role of domestic institutions, the

data of institutional quality is adopted from Institutional Quality database12. In this

database, institutions are classified into legal, political and economic ones. According

to Kucic (2012), legal institutions explain a large part of the formal institutions. As a

result, the absolute value of legal institution ( ) are taken as proxy for institutional quality of the recipient countries.

However, the other two kinds of institutions are also playing important roles in

explaining formal institutions. For instance, political institutions has a wide range of

influences on the rules and limits of the country, while economic institutions are

needed to secure legal system and enforcement of property rights (Kucic, 2012). Thus,

10

http://elibrary-data.imf.org/

11

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26 political institution ( ) and economic institution ( ) are adopted as proxies for the institutional quality in the robust check in section 5.7.

4.3 Control variables

This paper includes a series of control variables, which are widely used in

previous studies.

4.3.1 Domestic R&D intensity (RDI)

Domestic R&D expenditure plays an important role in explaining productivity

growth (Gianfranco, et al, 2012). Therefore, this paper adopts domestic R&D intensity

as one of the control variables to correctly estimate the spillover effects from abroad.

As specified before, the data of R&D intensity comes from the World Bank’s World

Development Indicators13, supplemented by the Eurostat14 and national statistics.

4.3.2 Human capital (HC)

Countries differ among themselves in terms of human capital. Human capital is

able to directly affect productivity as a factor of production, and indirectly influence

productivity by facilitating technology transfer (Bulent, 2008). As a consequence, this

13

http://data.worldbank.org/indicator/GB.XPD.RSDV.GD.ZS

14

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paper includes human capital as the second control variable. In this case, the tertiary

enrollment as a percent of the gross is used as a proxy of human capital, which is from

the World Bank15.

4. . Government expenditure (GE)

Government expenditure seems to have a significant relationship with economic

growth. Kojo and Yemane (2013) find robust evidence of a long-run positive

relationship between government expenditure and GDP. However, James and (2011)

address that government expenditures are estimated to have negative growth effects

for some developed countries. Although the relationship between government

expenditure and economic growth is still a subject of debate, this paper would like to

control for the possible impact of government expenditure on total factor productivity.

The data of government consumption as a share of GDP is adopted from the Penn

World Table16 8.0.

. Empirical analyses

This section provides descriptive of the variables, which provide the information

on the characteristics for all the data in the sample. Moreover, the diagnostic checks

are conducted in order to check the relationship between variables and the validity of

the model. In addition the regression results of the estimation as well as robust check

are presented.

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28 5.1 Descriptive statistics

Table 1 Descriptive Statistics

Variables Description Obs. Mean Minimum Maximum Std. Dev

Log total factor productivity 777 5.355 3.678 7.412 0.683

Log FDI spillover from developing countries 269 -2.082 -13.435 4.814 2.620

Log FDI spillover from developed countries 415 -0.489 -6.267 4.892 1.595

Log trade spillover from developing countries 410 -2.338 -6.448 0.403 1.264

Log trade spillover from developed countries 656 -0.771 -4.451 14.633 2.691

Log total trade spillover 411 -0.666 -3.796 2.245 1.250

Log total FDI spillover 273 -0.102 -6.266 4.950 1.685

_{Legal institution quality} ₇₉₅ _0.708 _0.288 ₁ _0.172

_{Political institution quality} ₇₆₆ _0.687 _0.172 _0.928 _0.166

Economic institution quality 768 0.617 0.034 0.893 0.173

Log total trade spillover weighted legal institution quality 411 -1.053 -3.925 2.010 1.320

Log total trade spillover weighted polotical institution quality 411 -0.074 -3.979 1.981 1.300

Log total trade spillover weighted economic institution quality 411 -1.107 -4.230 2.020 1.312

Log total FDI spillover weighted legal institution quality 273 -0.414 -6.389 4.637 1.679

Log total FDI spillover weighted political institution quality 273 -0.435 -6.431 4.657 1.677

Log total FDI spillover weighted economic institution quality 273 -0.481 -6.644 4.603 1.699

Human capital 716 46.732 2.847 103.873 21.301

Government expenditure share of GDP 777 0.188 0.059 0.459 0.063

Domestic R&D intensity 739 1.410 0.010 5.887 1.023

Source: Author’s own calculation, the World Bank, the Penne World table 8.0, the Eurostat and national statistics.

As shown in table 1, the variation of the dependent variable is not obvious,

which might due to that the logarithm form of total factor productivity is used in this

analysis. Among the sample countries, the United States outperforms other countries

with the highest number of (7.412) in 2010, however, Romania shows the

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In terms of spillover variables, the number of observations falls a lot, especially

relative to FDI spillover from developing countries (269 observations of ).

This is due to the limitation of availability of bilateral FDI and R&D intensity data.

However, there is relatively large level of variation of the spillover variables. For

instance, the number of varies from -13.435 in Iceland at 2000 to 4.814 in

Spain at the year 2010. Besides, Iceland also received the lowest level of FDI

spillover from developed countries ( -6.267), while the largest number of

FDI spillovers originated from developed countries goes to Japan in 2005

( 4.892). As to trade spillovers, ranges from -6.448 (Iceland,

2008) to 0.403 (US, 2003), whereas Ukraine received the lowest level of trade spillover from developed countries in 1994 ( -4.451), and much of this term is distributed to France in 2000 ( 14.633). It is obvious that the largest numbers of spillovers are all distributed to developed countries, which might

contribute to their higher than developing countries. In addition, the interaction terms of institutional quality and spillover variables presents relatively

large variation.

5.2 Fixed and random effect estimation

The panel estimation can be conducted via either random or fixed effects. The

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30 estimation assume that all the differences within countries are captured by the

intercept parameters, but regard the differences between countries as random; while

the fixed effect model assume that the intercept captures all the differences between

countries overtime. In this paper, both random and fixed effect estimation are adopted.

5.3 Normality

Hypothesis test and estimation for the coefficients rely on the assumption that the

dependent variable is normally distributed (Hill, et al. 2011). In order to estimate the

distribution of dependent variable, the Kolmogorov-Smirnov Test is used in this paper.

In this test, if the p-value is larger than 0.05, then the dependent variable is normally

distributed. By conducting the Kolmogorov-Smirnov Test in the Stata, the p-value is

observed as 0.128, which implies that the distribution of the dependent variable is

approximately normal. The normality distribution of can also be observed in figure 1. Figure 1 Distribution of lnTFP 0 20 40 60 80 1 0 0 F re q u e n cy 4 5 6 7 8

log total factor productivity

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5.4 Multicollinearity

Before the regression, another concern is the multicollinearity problem, which

happens when two or more explanatory variable have a linear relationship (Hill, et al.

2011). It is a major obstacle in the estimation, since this problem will cause bias of

coefficients. In this case, the correlation matrix is used to investigate whether

variables are potentially correlated with each other. More specifically, if the absolute

value of the correlation between each pair of explanatory variables are larger than 0.8,

the multicollinearity problem occurs (Hill, et al 2011).

Table 2 Correlation matrix

variables 1.000 0.310 1.000 0.381 0.627 1.000 0.645 0.545 0.474 1.000 0.530 0.268 0.343 0.429 1.000 0.142 -0.229 -0.173 -0.147 -0.134 1.000 -0.471 0.089 0.141 -0.086 0.046 -0.144 1.000 0.375 -0.117 0.038 0.073 0.201 0.553 -0.050 1.000 0.736 0.494 0.558 0.853 0.535 -0.254 -0.049 0.170 1.000 0.390 0.797 0.935 0.508 0.321 -0.180 0.136 -0.005 0.558 1.000 0.191 -0.163 -0.069 -0.264 0.063 0.508 -0.069 0.632 -0.099 -0.080 1.000 0.053 -0.144 -0.099 -0.307 -0.025 0.574 -0.003 0.471 -0.259 -0.096 0.908 1.000 _0.191 _-0.067 _0.038 _-0.192 _0.065 _0.492 _0.037 _0.447 _-0.057 _0.025 _0.880 _0.880 _1.000

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32 As shown in the correlation matrix (table 2), in the basic model and the

alternative models, none of the explanatory variables are significantly correlated to

each other.

Another approach is to use the Variance Inflation Factor (VIF). In this estimation,

if the value of VIF is larger than 10, there is a linear relationship within explanatory

variables and multicollinearity exists (UCLA, 2010). The results are shown in the last

row of table 3 and table 4 in section 5.6. The value of VIF is less than 10 in the basic

model, but extremely high in the alternative models (column [6] and [7]). However,

this is due to that an interaction term is added in these two estimations. Hence, the

results of this study will not be interrupted by the multicollinearity problem.

5.5 Heteroskedasticity

Heteroskedasticity exist when the variances for all observations are not the same.

It would result in biased standard errors, which in turn lead to that the confidence

intervals and hypothesis tests that use these standard errors are misleading.

The White test is employed to provide some statistical evidence about

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which means that the null hypothesis of this test is rejected and the estimations of this

paper are biased by heteroskedasticity. In this case, the significance level of the

explanatory variables will be under- or over-estimated, which result in incorrect

understanding of the relationship between explanatory variables and the dependent

variable.

In order to provide solution for the heteroskedasticity problem, the White robust

standard errors will be used, which is helpful to correct the P-values and standard

errors in the regression (Hill, et al. 2011)

. Regression results

The regression results are presented in table 3 and table 4. Table 3 presents the

empirical results by using random effect estimation, while table 4 shows the results of

fixed effect estimation. In both table 3 and table 4, the values of R-square are all

larger than 0.60, which means that these models have good explanatory power, since

more than 60% variation of the dependent variable is explained by variables in the

right side of the equations.

. .1 Technology spillover from developed and developing countries

Firstly, columns [1]-[4] show the results of the basic model (equation 1), which

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34 Specifically, column [1] emphasizes on the effect of technology spillover from

developed countries on productivity. The coefficient of is positive and

significant relative to total factor productivity in both random and fixed effect

estimation. Meanwhile, when technology spillover from developing countries is

considered instead of that from advanced countries, as shown in column [2], the

coefficient of ln is positive and significant at the 1 percent level. In addition,

when technology spillovers originated from developing and developed country are

pooled together (column [4]), the coefficient of and ln still

remain positive and significant. This is the evidence that there is a strong positive

relationship between trade spillovers and productivity.

However, it is surprise to find that the coefficients of ln is larger than that of , as shown in column [4], which means that the influence of trade

spillover originated from developing countries are larger than that from developed

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Table 3 Estimation results (Random Effects)

Basic model Alternative model 1 Alternative model 2

Equation (1) Equation (8) Equation (9)

Variable 1 2 3 4 5 6] 7 ln 0.002 0.003 (0.004 (0.004 ln 0.158 0.162 (0.053 (0.055 ln 0.010 0.007 (0.004 (0.007 ln 0.010 0.011 (0.006 (0.005 0.004 0.008 0.005 0.005 0.004 0.004 0.004 (0.002 (0.001 (0.001 (0.001 (0.001 (0.001 (0.001 0.034 0.093 0.182 0.181 0.158 0.152 0.152 (0.030 (0.040 (0.060 (0.059 (0.046 (0.043 (0.043 -0.033 -1.15 -1.343 -1.553 -1.172 -1.135 -1.135 (0.028 (0.766 (0.731 (0.727 (0.609 (0.624 (0.624 0.006 -2.118 0.007 (0.007 (1.025 (0.006 0.245 0.229 -1.896 (0.039 (0.037 (1.014 -3.077 -3.077 (1.609 (1.609 2.125 (1.025 2.125 (1.025 constant 5.074 5.181 5.601 5.680 5.437 8.403 8.403 (0.129 (0.152 (0.210 (0.205 (0.134 (1.566 (1.566 Obs. 690 355 215 206 217 217 217 2 _0.685 _0.649 _0.622 _0.625 _0.613 _0.651 _0.651 VIF 1.000 1.120 1.330 1.460 1.300 1516.200 739.350

Note: The dependent variable is logarithm of total factor productivity. Figures in parentheses are standard deviations. ***, ** and * indicate significant at the 1 percent, 5 percent and 10 percent levels, respectively.

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36

Table 4 Estimation results (Fixed Effects)

Basic Model Alternative model 1 Alternative model 2

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Besides, relative to FDI spillovers of the basic model, the results are ambiguity.

In column 2 of table 3 the coefficient of ln is observed to be positive and

significant, however, the coefficients of other FDI spillover variables are all

statistically insignificant. As a result, this paper does not find strong empirical

evidence to prove that technology spillover via FDI from developed countries has a

larger positive influence on productivity growth than that from developing countries,

and hypothesis 2 is not supported in general.

5.6.2 The role of institutional quality

Column [5]-[7] present the empirical evidence relative to how institutional

quality of recipient countries influence the relationship between technology spillover

and productivity. Columns [6] are relative to the alternative model 1 (equation 8),

which is interested in the role of institutional quality in FDI spillover-productivity

relationship. In addition, columns [7] investigate the influence of institutional quality

on the relationship between trade spillover and productivity, as described in the

alternative model2 (equation 9).

Initially, according to column [5] of both tables, without considering the role of

institutional quality, the coefficients of total FDI spillovers ( ) are statistically

insignificant relative to total factor productivity. However, as shown in columns [6],

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38 ( ) is positive and significant, which indicates that foreign FDI becomes beneficial for recipient countries when the level of domestic institutional

quality is high. Thus, hypothesis 3a is confirmed.

Moreover, column [5] also presents that regardless of institutional quality, the

coefficient of equals to 0.245 in the random effect estimation and 0.157 in

the fixed effect estimation. Both of them are statistically significant. However, as

shown in column [7], the coefficient of interaction term between trade spillover and

institutional quality ( ) is significant and increase to 2.125 in table 3 and 2.526 in table 4. This is the evidence that the effects of trade spillover on productivity will be larger when recipient country has a higher level of institutional

quality. Hence, hypothesis 3b is confirmed.

Refers to control variables of this paper, human capital has a significant and

positive influence on productivity. Moreover, as demonstrated by previous studies,

domestic R&D intensity remains an important factor for growth, since most of the

coefficients of are positive and significant. In addition, this paper observes an ambiguity relationship between government expenditure and productivity, since in the

random effect estimation, the coefficients of are significant and negative, while that in the fixed effect estimation is statistically insignificant.

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The next step is to check the robustness of above result. For the basic model, this

paper tends to do this by adding a one-year lag to variables in the right-side of the

equations. There are mainly three reasons.

Firstly, relative to trade spillover, domestic firms may need a period of time to

imitate and learn foreign technologies and apply it into production; in the process of

FDI spillover, there is a lag of anywhere from one to four years before new ventures

become profitable (Kentor 1998). In another word, new investment projects do not

usually generate profits immediately. Secondly, according to Borensztein, et al.

(1998 human capital and FDI is considered to be complementary in the process of

technology diffusion, thus human capital needs to be taken in analysis within the same

time as FDI, and this mechanism can also be applied in domestic R&D intensity and

government expenditure. Thus, all control variables are applied a one-year lag.

Thirdly, lagged values can be used to deal with the potential endogeneity problem

(Fritsch, 2002).

With a one-year lag, the basic model shift to equation (13).

1 1 2 1 1

1 1 (13)

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40 The results of random effects estimation for equation (13) are shown in columns

[1]-[4] of table 5. In this estimation, the effects of FDI and trade spillovers as well as

control variables remain the same signs and similar significant level as the results

observed without using lagged variables.

Table 5 Robustness check estimation results (Random Effects)

Basic model Alternative model 1 Alternative model 2

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1.537 (0.780 1.625 (0.768 constant 5.027*** 5.157*** 5.582*** 5.651*** 5.437 7.930 8.076 7.930 8.076 (0.136) (0.156) (0.227) (0.222) (0.134 (1.294 (1.162 (1.294 (1.162 Obs. 690 355 215 206 217 217 217 217 217 2 _0.651 _0.677 _0.635 _0.640 _0.613 _0.628 _0.657 _0.628 _0.657 VIF 1.000 1.120 1.330 1.460 1.300 604.740 2029.920 284.900 971.360

However, there are significant differences in the fixed effect estimation relative

to the effect of trade spillover on productivity, which presents some clues for why the

impact of trade spillover from developing countries is larger than that from developed

countries, as obtained in table 4.

According to columns [2]-[4] of table 6, when a one-year lag is applied in

regression, the coefficient of trade spillover from developing countries (ln ) becomes insignificant, while that from advanced countries (ln ) becomes positive and statistically significant. According to Timothy, et al. (2012) in the

process of fragmentation, developing countries concentrate on low value-added and

low-technological activities, while developed countries specialized in high-skill and

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42 developing countries are relatively easier to adopted and imitate in the short term,

while technology originated from advanced countries requires more complex learning

process and significant adaptation. Thus, when this study does not consider lag values,

trade spillover from developing countries seems to benefit productivity more,

however, when a lag value is involved in this study, trade spillover from developed

countries are demonstrated to have a significant and positive influence on productivity

growth.

The estimated results of FDI spillovers variables are similar as in table 3 and

table 4. These findings are quite different from previous studies which demonstrate

that FDI is a significant channel for technology spillovers. This might be due to that

distinct country sample and time period are used in the analysis. Another explanation

lies in that the benefit effect of FDI is influenced by other factors, such as the entry

mode of FDI (Wang and Wong, 2009) and the development of financial system in the

recipient countries (Hermes and Lensink, 2003), which is beyond the scope of this

research.

Table 6 Robustness check estimation results (Fixed Effects)

Basic Model Alternative model 1 Alternative model 2

Variable 1 2 3 4 5 6 7 8 9

ln -0.000 0.000

(0.006) (0.004

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44 In the robustness check for the alternative models, this paper uses alternative

indicators for institutional quality, as specified in section 4.2.3. In this case, legal

institution ( ), is replaced by political institution ( ) and economic institution ( _{). The results are shown in columns [6]-[9] of}

table 5 and table 6.

The results for the estimated effects of FDI and trade spillovers are robust and

similar to the ones obtained by using legal institution ( ). More specifically, when and are adopted in the estimation, their interaction term with FDI spillovers ( ,

_{) are positive and significant. Besides, the coefficients of}

interaction term with trade spillovers ( ,

_{) are also significant and become larger.}

6. Conclusion

During the past few decades, many researchers have paid a lot of attention on the

relationship between technology spillover and productivity development, since in the

trend of globalization, foreign advanced technologies become increasing important,

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Since there are significant differences between countries in term of economic

development, quantity and quality of FDI and international trade as well as

institutional quality, this paper focus on investigating whether technology spillover

originated from developed countries has bigger positive influence on productivity

development than that from developing countries. Besides, this paper also aims to

provide some empirical insight about how domestic institutional quality influences the

spillover effects of trade and FDI.

Using a panel dataset of 37 countries from 1990 to 2010, this paper

observed that trade spillover have a robust positive relationship with productivity,

which is similar with previous studies (Coe & Helpman, 1997; Krammer, 2008).

However, in our estimation, trade from developing countries has a larger spillover

effects than that from developed countries, which might be due to that among trade

(FDI) partners in my sample, the technologies originated from developing countries

are easier for other countries to adapt in production than that from advanced countries,

especially in a strong trend of fragmentation.

Furthermore, this paper does not find a strong positive and significant

relationship between FDI spillover and productivity, which is different from previous

studies (Hejazi & Safarian, 1999; Xu, 2000; Krammer, 2008). This might be caused

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46 important factors, such as the entry mode of FDI (Wang and Wong, 2009) and the

development of financial system (Hermes and Lensink, 2003).

Moreover, institutional quality of the recipient country is a critical factor in

spillover process since it is able to enhance technology spillover of trade and FDI. In

addition, my findings also confirmed that human capital and domestic R&D

expenditure plays an important role in explaining productivity level.

However, this paper is marked my several important limitations. A considerable

limitation is the lack of data source for bilateral FDI and R&D intensity, especially for

developing countries. Under this situation, our sample size is limited, which may also

prevent this study to observe a significant relationship between FDI spillover and

productivity. Another limitation is about the measurement of technology spillover. In

this study, technology spillover is measured by FDI- and trade-weighted R&D

intensity. However, since there is no standard measurement of spillover, it is hard to

judge whether this measurement can fully capture the amount of technology

spillovers.

Future studies can contribute to technology spillover issue by further distinguish

types of countries to advanced countries, emerging countries and poor countries,

because as addressed by Gary and Karina (2011), advanced countries and emerging

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poor countries. Furthermore, another way to distinguish countries is to group them

within or out of a trade agreement, since the flow of trade and FDI seems to have

different pattern towards countries in and out of a trade agreement.

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International technology spillovers from trade and FDI: a comparison between advanced and developing countries

International technology spillovers from trade and

FDI: a comparison between advanced and developing

countries

Contents

International technology spillovers from trade and

FDI: a comparison between advanced and developing

countries