FDI and ICT Innovation Effects on Productivity Growth: A ... II/II.D... · An emerging body of empirical literature is concerned with how FDI affects labor productivity and economic

FDI and ICT Innovation Effects on Productivity Growth:

A Comparison between Developing and Developed Countries

Sotiris K. Papaioannou*

Abstract

This paper investigates for possible innovation effects stemming from Foreign Direct

Investment (FDI) and Information and Communication Technologies (ICT) on productivity

growth. An augmented production function was estimated using a sample of developing

and developed countries in 1993-2001. A uniform positive and significant innovation effect

from FDI was established for all countries, while divergent results between developing and

developed countries were obtained for ICT and the interaction effects of FDI. These results

are robust to possible endogeneity and omitted variable problems. They also suggest that

the level of development matters in estimating such impacts.

JEL classification: F21; O30; O47

Keywords : FDI; ICT; Innovation; Productivity

(The paper is competing to the Young Economist Award)

* Ph.D. Student, Athens University of Economics and Business, 76 Patission Street, 10434 Athens, Greece e-mail: [email protected]. **I am grateful to the Greek State’s Scholarship foundation for its financial support. 1

1. Introduction

The rapidly rising level of economic integration, stimulated by advances in

transportation as well as in information and communication technology, renders technology

adoption, coming from foreign developed countries, a matter of great importance for

economic growth and productivity improvement. Furthermore, economic theory suggests

that learning through international economic activity might be particularly important for all

countries, especially for those lagging behind the most developed ones.

Foreign Direct Investment (FDI) is considered, among others, an important channel

for technology diffusion. Multinational enterprises possess superior technology and

management techniques, some of which are captured by local firms when multinationals

locate in a particular economy. Other sources of positive FDI effects are forward and

backward linkages between multinational and domestic firms, as well as the host country’s

access to specialised intermediate inputs, which in turn raise the economy’s total factor

productivity. Furthermore, the new ‘information economy’ of the last decades is associated

with increased diffusion of Information and Communication Technologies (ICT) which is

expected to deliver higher productivity gains and enhanced growth1.

An emerging body of empirical literature is concerned with how FDI affects labor

productivity and economic growth in host economies. Most studies in this literature have

been conducted at the micro-level using firm-level or industry data and are usually limited

to manufacturing industry. The existing empirical evidence is mixed, depending on the type

of data examined (cross-sectional versus panel data), the level of development of the FDI

recipient country, the econometric analysis employed and the research design. Fewer

studies have been conducted at the macro or international level given the lack of long time-

1 See the review paper by Blomstrom and Kokko (1998) for the FDI spillover effects on labor productivity and growth. Also, see Daveri (2002) for a recent review on the ICT growth effects. 2

series data on FDI and relevant country or industry characteristics. On the other hand,

recent evidence on the growth contribution of ICT capital is also mixed with the so-called

‘productivity paradox’ remaining still unresolved. Thus, as richer data are becoming

available for longer periods and more countries, the macro-economic effects of technology

transfer through FDI and ICT become appealing.

The aim of this study is towards this direction: to search for innovation effects on

aggregate productivity growth generated by either FDI technology transfer or/and ICT

investment. A production function is employed to investigate these effects using a panel of

developing and developed countries in the period 1993-2001. The study of this period

attracts a special interest given the sizable increase in world FDI after the major political,

financial and economic reforms of the 1990s.

Evidence is provided for a positive and significant impact of FDI on productivity

growth in both developing and developed countries. A uniform positive and significant

innovation effect from FDI on growth was established for all countries, while divergent

results between developing and developed countries were obtained for ICT and the

interaction effects of FDI. These results are robust to possible endogeneity and omitted

variable problems. They also suggest that the level of development matters in estimating

such impacts.

The rest if this paper is organized as follows. Next section summarizes the results of

the relevant literature. Section 3 contains the econometric specification of the model. In

section 4 the data are described and some descriptive statistics are presented, while section

5 provides the empirical results. Finally, section 6 concludes.

3

2. Related Literature

An emerging body of literature considers how production externalities, arising from

FDI, affect the host economies. So far, the existing evidence on the impact of FDI, as a

mechanism of technology diffusion, is mixed. However, serious econometric problems

characterise most of the cross sectional studies, such as the endogeneity issue and the

omitted variables bias. In a relatively early study on some OECD developed countries,

Barrell and Pain (1997) suggest that there is evidence for significant spillovers and

increased export performance from the presence of inward FDI. In a related work,

Borensztein et al. (1998), using a panel of 69 developing countries in the 1970s and 1980s,

found a significant positive effect of FDI on growth only for countries with a minimum

threshold stock of human capital. These results suggest the importance of the absorptive

capacity of the host economies in assimilating the advanced technologies transferred,

usually from developed countries, a hypothesis thoroughly explored in relevant micro-

studies.

Hejazi and Safarian (1999) estimated that FDI is a dominant channel for R&D

diffusion in OECD countries with its importance being higher than that of trade. However,

de Mello (1999) using both time-series and panel data techniques in a number of OECD

and non-OECD countries, during the period 1970-1990, provided evidence that the extent

to which FDI is growth-enhancing depends on the complementarity or substitutability

between FDI and domestic investment. Furthermore, Balasubramanyam et al. (1999)

suggest that an important role is exerted by the size of the local market, the competitive

environment and the availability of human capital in order for FDI to promote economic

growth, while Elahee and Pagan (1999) find positive evidence for the role of FDI in East

Asian and Latin America countries, over the period 1985-1993.

4

Barthelemy and Demurger (2000), using panel data on 24 Chinese provinces in the

period 1985-1996, provide evidence for a positive and mutual relationship between FDI

and economic growth. Furthermore, they stress the importance of human capital for the

adoption of foreign technologies and economic growth. Haveman et al. (2001), using data

from 1970 to 1989 and 74 countries, find evidence for a positive effect of international

integration indicators, such as openness, membership in a trade block and FDI, on

economic growth. In addition, they suggest that these indicators are significantly correlated

and should be examined together in order for their estimated impacts to be robust. By

contrast, Zhang (2001), in a study of 11 East Asian and Latin America countries during the

period 1960-1997, finds that there is a strong variation in the growth enhancing impact of

FDI. According to his findings, FDI is more likely to boost economic growth in countries

with particular characteristics like liberalised trade regimes, improved education, large

export-oriented FDI and macroeconomic stability, e.g. Hong Kong, Indonesia, Singapore,

Taiwan and Mexico. Further evidence in favour of a positive growth FDI effect is provided

by Ram and Zhang (2002) using a cross section of 85 countries between the years 1990 and

1997, and Campos and Kinoshita (2002) utilising panel data of 25 transition economies in

the period 1990-1998.

Regarding the impact of ICT on growth and productivity, the existing evidence

shows mixed results. Most recent estimates converge to the conclusion that, at least for

USA and high technology sectors, this effect is positive and significant. For the remaining

European and other world countries, the results are not conclusive. Schreyer (2000) and

Daveri (2002) examine the contribution of ICT on G7 and European countries,

respectively, and show that there do not exist powerful signs for beneficial effects on

productivity. In an empirical study of the period 1985-1993, Dewan and Kraemer (2000)

5

come to the conclusion that the developed countries enjoyed substantial gains and achieved

an increase in their output by the use of ICT. On the contrary, the developing countries do

not benefit from essential returns and this is justified by the lack of additional infrastructure

investments. Finally, Gust and Marquez (2004), in their study of the period 1992-1999,

show that productivity growth has slowed in a number of industrialized countries due to

regulations affecting labour market practices which have impeded the adoption of

information technology.

3. Econometric Specification

3.1. Production Function

To capture the effect of FDI on productivity growth, a production function is

specified with several types of inputs. The present study considers FDI as a special type of

knowledge and technology capital introduced in the production process. Consequently, the

regression analysis will be carried on by decomposing the overall effect of total capital to

that of its individual domestic and foreign components2.

In order to capture the FDI and ICT effects, the paradigm of Hall and Mairesse

(1995) will be followed in specifying an aggregate Cobb-Douglas production function,

which incorporates four inputs, domestic capital (K), labour (L), foreign capital (F) and

ICT capital:

Yit = A ect(Kit)α(Lit)β(Fit)γ(ICTit)δ euit (1)

where the subscripts of i and t denote country and year, respectively; Y measures gross

output of each country, A and c are constant parameters, while t is a time trend. Parameters

α, β, γ and δ are the elasticities of domestic capital, labor, foreign capital and ICT with

2 In the subsequent sections, the terms of FDI and foreign capital are used interchangeably. 6

respect to output and finally uit is the error term capturing unobserved variations between

countries and over time.

After taking logarithms and following the assumption of constant returns to scale,

the level of output per worker can be expressed as a function of domestic capital, foreign

capital and ICT to labour ratios:

ln( + 1 ln( ) + ∂ 2 ln( ) + 3 ln( ) + (2) ctAyit += ln) ∂ itk itf ∂ itict itu

where small case letters denote figures per worker, while the parameters 1, 2, 3

represent the elasticities α, γ, and δ, respectively. Since the goal is to estimate a growth

equation, the first differences of the above equation are taken to obtain the following

form:

∂ ∂ ∂

lnyit - lnyit-1 = c + 1(lnkit - lnkit-1) + 2 (lnfit - lnfit-1) + 3 (lnictit - lnictit-1) + (3) ∂ ∂ ∂ itε

The above formulation is further augmented by a number of other variables proposed by

the new growth theory (Mankiw et al., 1992). Thus, the lagged level of output per worker

(in its logarithmic scale) is introduced, to capture the catch-up effect among countries, as

suggested by Barro (1997). Human capital is, also, introduced, the importance of which

may be strong for economic growth, as Barro (1991) has already found for a cross section

of 98 countries in the period 1960-1985. Other control variables include the transparency

indicator, the government share of GDP and the openness of each country to international

trade, defined as the ratio of total imports and exports to GDP.

3.2. Fixed Effects or Random Effects?

When dealing with panel data, it is typical to view the unobserved factors affecting

the dependent variable as consisting of two types: those that are constant and those that

vary over time. Consequently, the following structure of the error term is specified:

7

= ηi + (4) itε itα

The term ηi is an unobserved time-invariant country effect, while is the idiosyncratic

error that varies independently across countries and time, assumed to be uncorrelated with

the other explanatory variables (Hsiao, 1986; Johnston and Dinardo, 1997). Therefore, the

cross country time-varying growth equation can be rewritten as:

itα

lnyit - lnyit-1 = c + ∂ 0( lnyit-1) + ∂ 'Χit + ηi + (5) itα

where Χit denotes the vector of explanatory variables included in (3) as well as all control

variables mentioned earlier, except the lagged per worker GDP, and ∂ the vector of the

corresponding parameters. Depending on the assumption about the correlation between the

cross-section effect ηi and the explanatory variables, two empirical models can be specified

based on whether the random effect or the fixed effect estimator is used. The former one is

more efficient but it is based on the assumption that the country effect, ηi, is not correlated

with the vector of the explanatory variables X. Furthermore, the latter one is more

consistent since it does not require the existence of this assumption but, on the other hand,

is less efficient due to loss of variation in the data by the imposition of country dummies.

Since the choice of any of these two techniques implies a consistency–efficiency trade off,

the best strategy followed by many researchers is to test whether the difference between the

random effect and the fixed effect estimates is significantly different from zero. After

applying the Hausman (1978) specification test3, the results indicate that the difference

between the random effect and fixed effect estimates is statistically significant indicating

that the best strategy would be the employment of a fixed effect estimator.

3 The Hausman statistic is distributed as a chi-square variable whose value reaches 351.98 (p-value: 0.00) when the initial hypothesis is that the difference in coefficient estimates is not systematic. 8

3.3. Endogeneity Issues

Although the basic motivation of most of the existing theoretical and empirical

work is the potential effect of FDI on economic growth, the association between GDP

growth and FDI does not mean that causality runs from one direction. The direction of

causation may run either way.

To correctly assess the empirical relationship between productivity growth, FDI,

ICT and other variables included in the vector of explanatory variables, X, the generalized

method of moments (GMM) estimator is used, as developed by Arellano and Bond (1991).

They propose to differentiate equation (5) which becomes:

(lnyit – lnyit-1) - (lnyit-1 – lnyit-2) = 0 (lnyit-1 – lnyit-2) +∂ '(Xit - Xit-1) + (αit -αit-1) (6) ∂

While differencing eliminates the country specific effect, a new bias is introduced

by the construction of the error term, αit–αit-1 which is correlated with the lagged dependent

variable, lnyit-1–lnyit-2. However, after accepting that the error term is not serially correlated

and that the set of explanatory variables, X is weakly exogenous, that is to say that the

explanatory variables are not correlated with future values of the error term, Arellano and

Bond (1991) propose the following moment conditions:

E[(lnyit-s–lnyit-s-1)*(αit–αit-1)]=0 for s≥ 2; t= 3,...,T (7)

E[Xit-s(αit – αit-1)] = 0 for s ≥ 2; t = 3,...,T (8)

Using these moment conditions, Arellano and Bond propose a GMM estimator which uses

the lagged values of some explanatory variables as instruments in a differenced regression

equation. These explanatory variables are treated as predetermined, in that it is supposed

that past values of the disturbance term have some impact on their future realizations.

The consistency of the GMM estimator is based on the validity of the instruments

used in the differenced regression and the absence of second order serial correlation in the 9

error term. For this reason, Arrelano and Bond (1991) propose two specification tests. The

first one is a Sargan test of over-identifying restrictions which tests for the validity of the

instruments used in the regression. The second one is a test which examines for second-

order serial correlation4. Failure to reject the null hypotheses of both tests gives support to

the above model.

4. Data and Descriptive Statistics

A panel of 43, developed and developing, countries, in the period 1993-2001, was

constructed for this empirical application. The required data were taken from a variety of

sources. GDP data were taken from Penn World Tables (Heston et al., 2002) and the World

Bank (2003) database. Capital stock data were estimated using the perpetual inventory

method and gross investment figures from IMF (2003)5. The initial values of the capital

stock series were taken from Penn World Tables (Heston and Summers, 1991).

Since data on total fixed investment of a particular country includes FDI, the capital

stock series constructed as above would be correlated with the FDI series. Furthermore,

physical capital is a stock variable, so it would not be correct to include FDI as a flow

variable. Subsequently, a procedure was followed to break down the capital series into its

domestic and foreign component. More specifically, the value of foreign capital was

approximated by utilizing the share of FDI stock to GDP, as published by UNCTAD

(2003a). Domestic capital was then obtained by subtracting the value of foreign capital

stock from that of total capital stock.

4 By construction, the differenced error term is first order correlated, but this does not imply that so does the original error term. 5 In countries for which no initial estimate is given, the capital stock variable is calculated as the sum of gross investments that have been realized until previous year minus their accumulated depreciation. The depreciation for each year is calculated using the Winfrey mortality function. 10

Data on ICT capital are not existent. Instead, data on ICT spending are provided by

the World Information Technology and Services Alliance (WITSA, 2002) which can be

used to capture the ICT effects. The ICT spending data comprise of household

consumption, public consumption and private investments. Furthermore, the data regarding

the number of workers were taken from the International Labor Organization (ILO, 2003).

Human capital was approximated by male secondary enrollment rates obtained from

World Bank (2003), while the government share of GDP is taken from Penn World Tables

(Heston et al., 2002). The transparency index and the openness indicator were taken from

Transparency International Organization (2004) and Penn World Tables (Heston et al.,

2002), respectively. All value variables are expressed in purchasing power parity (PPP) in

order to make the data compatible across countries

It should be made clear that the number of countries included was determined by

the availability of data on both FDI and ICT, which are the variables this paper focuses on.

With this in mind, first a description of the data is made and then follows the regression

analysis. Tables 1-3 contain descriptive statistics for the variables and group of countries

under investigation. According to Table 1, developing countries have increased their share

on world FDI, at the expense of the most developed ones, with the exception of some East-

Asian countries, which have witnessed an important decrease, due to the 1997 financial

crisis. A special case worth mentioning is that of China, being now the first FDI recipient

economy in the world. Overall, inward FDI seems to have gained more importance as an

investment mechanism, since its percentage share on Gross Fixed Capital Formation has

increased from 4.4 % in the period 1991-1996, to 12.8% in 20016, as reported by UNCTAD

(2003b).

6 Similar conclusions are drawn for outward FDI, since its share has increased from 5% in the period 1991-1996 to 11.3% in 2001. 11

Table 2 contains descriptive statistics, for all variables that will be employed in the

econometric analysis. As it is evident, the developing countries exhibit higher FDI, ICT and

capital growth rates, while their GDP per worker growth is similar to that of the developed

countries. Apparently, a longer time period might be necessary in order for the developing

countries to reap the benefits of their investments. Finally, it appears that the developing

countries exhibit higher government presence in their economies and seem to adopt less

liberalized trade policies, which may have negatively affected their economic growth.

5. Regression Analysis and results

5.1. Initial Results

Regressions are performed on a pooled cross-section time-series data set consisting

of 43 countries in a nine year period (1993-2001). Annual labour productivity growth is

regressed on a number of explanatory variables suggested by growth theory. Equation (5) is

estimated using the fixed effects methodology, the results of which are presented in tables

4-6.

The baseline regression in each table (column 1) includes the lagged level of output

per worker (YL), two forms of capital inputs: domestic and foreign capital growth per

worker (GKD and GKF), and a proxy for the human capital variable (SCHOOL). As it is

evident from table 4, the elasticity of both forms of capital is highly significant with that of

domestic being much higher as expected, the catch-up effect is negative and significant

implying income convergence, while the impact of schooling on growth is negative but

insignificant. Next column reports results based on the initial regression with the addition

of the growth rate of ICT spending per worker (GICT)7 and three control variables: an

7 Because of lack of ICT capital data, the ICT spending variable was used instead. In this case, the coefficient does not measure elasticity, but the return to productivity of ICT spending. 12

indicator referring to the level of transparency of the corresponding country (TI), the degree

of openness measured by the share of total trade to GDP (OPEN) and the government share

of GDP (GOVSH).

It is interesting to notice in column 2 the positive and significant, at the 10% level,

ICT growth effect, something that has long been disputed in the empirical literature. The

inclusion of the other three variables did not affect much the previous estimates, while

openness and the level of transparency seem to exert a significant impact on growth. The

explanatory power of the model improves (R2 = 0.30) and remains satisfactory for this type

of analysis. The government share of GDP variable shows no significant correlation with

economic growth as found in other empirical studies.

Columns 3-7 report the estimation results after introducing interaction terms of

foreign capital growth with domestic capital growth, ICT spending growth, openness of

trade and human capital. It can be noted that the previous parameter estimates remain

robust across the alternative regressions, with the exception of foreign capital, the

magnitude of which is increasing substantially. More discussion on the interaction terms is

given in a separate section that follows.

5.2. Differences between Developing and Developed Countries

A Chow test for the equality of coefficient estimates between the developing and

developed countries was rejected. For this reason, separate regressions for the two sub-

samples (developing and developed countries) were performed, the results of which are

presented in tables 5 and 6, respectively.

Concerning the developing countries (table 5), we can easily notice that the

domestic capital effect is of equal importance, as compared to the full panel of countries.

13

Furthermore, the foreign capital impact is large and positive in most of the regressions.

Only in column 6 the foreign capital coefficient was negative, a fact that can be attributed

to the presence of its interaction with schooling, the variation of which is very small in the

sample.8 The importance of ICT is also positive, but no conclusive inference can be drawn

since its significance is small. Furthermore, all the other control variables (transparency,

openness indicator and government share) exert a positive and mostly significant impact on

productivity growth.

With respect to the developed countries (table 6), a first reading of the results

reveals that the coefficient of domestic capital intensity is positive and significant and its

magnitude is greater than the one obtained from the entire panel. Similarly, the foreign

capital impact is positive and significant, in most of the alternative regressions, while its

magnitude is also increasing as more interaction terms are added in the baseline regression.

Furthermore, ICT shows a small negative but insignificant impact, the openness indicator a

positive and significant effect, while the government share coefficient is negative and

insignificant.

5.3. Discussion of the Results

The estimation results, obtained from the panel data analysis described above,

suggest that the accumulation of FDI contributes positively and significantly to the

productivity growth of countries, irrespective of their level of development. Overall, the

results indicate the rising importance of foreign capital, relative to that of domestic capital,

for economic growth. This is further justified by comparing the estimated coefficients of

foreign and domestic capital to their relative shares in total capital. As it is evident from

8 It should be reminded that a high degree of correlation between FDI and its interaction with schooling was observed in the data as shown in Table 3. 14

table 9, the effect of foreign capital on productivity growth is relatively high if is taken into

account its low share in total capital. It deserves to mention that, in the group of developing

countries, its impact on productivity growth is higher compared to that of domestic capital,

which is partially justified by its relatively higher share in total capital, indicating the

higher efficiency gains rising from the employment of FDI in these countries.

Regarding the innovation effects from ICT, the results provide some preliminary

evidence with regards to the importance of the ‘new economy’ for growth in the developing

countries. This fact opposes the main finding of Dewan and Kraemer (2000), supporting

that, mainly, the developed countries have benefited from the use of ICT. The present

evidence about the innovation effect from ICT is therefore inconclusive, and is in

accordance with many other studies in the literature that have failed to explain the

productivity paradox.

Among the other significant contributors to growth, the trade openness is

mentioned, which exerts a positive and significant impact on growth, confirming previous

similar evidence (Haveman et al., 2001). On the contrary, human capital, proxied by

schooling, had no influence on growth. However, the existing evidence is not conclusive

about the significance of human capital on growth. In an earlier cross country study,

Benhabib and Spiegel (1994) did not find any significant impact when human capital

entered the growth equation as a separate input. One of the difficulties in estimating growth

regressions with panel data is the measurement of human capital. The lack of long annual

time series data leads to the use of less appropriate proxies as the one used in this paper,

15

which may not capture properly the effect of education9. More discussion on this issue is

given in the following section.

5.4. Complementarity of Foreign Capital

One of the most arguable issues in the FDI-growth nexus is whether FDI-related

capital complements human capital or/and domestic investment. To investigate these

issues, interaction terms between foreign capital growth and domestic capital growth, ICT

spending growth, openness or schooling, were included in the regressions reported in

columns 3-7 of tables 4-6. When introducing interaction terms in a regression, collinearity

may result among them. In this case, foreign capital was found to be highly correlated only

when interacted with schooling10. A deeper analysis of their correlation indicates that this

was due to the small variability of schooling, the effect of which was insignificant in most

of the regressions. No serious correlation problems were created from the presence of the

other interaction terms.

As it is evident from the results in column 3, of tables 4-6, FDI interacts negatively

with domestic capital in developing countries, but positively in the developed ones. The

interaction effect, however, is significant only in the former ones as well as in the entire

sample. This finding could mean that foreign capital, embodying higher technological

advancement, cannot produce productivity gains, by complementing domestic capital, due

to the low absorptive capacity of the less developed host economy and, probably, due to

other institutional and cultural reasons. By contrast, in the case of a developed recipient

country, the technology gap is not that large to constrain complementarity with domestic

9 According to Barro and Lee (1994), the only measure of human capital that is most significantly correlated with growth is average years of male secondary schooling. However, this variable could not be used in this study as it is available on a five-year basis. 10 See correlation matrix in table 3. 16

capital. This implies that FDI may contribute to growth not only because it adds to

domestic capital, but also due to higher efficiency gains. Another possible explanation is

that foreign capital may not be deemed to be so much efficient or productive than the one

already employed in host economies.

These results are in contrast to the major findings of de Mello (1999) suggesting

that the degree of substitutability between domestic capital and FDI is higher in

technologically advanced economies. However, the period investigated in this paper (90’s)

is different, in many aspects, from the period (1970-1990) examined by de Mello.

Furthermore, de Mello (1997), in a survey paper, supports the view that the period

prevailed by complementarity may be short-lived. According to this aspect, a

Schumpeterian view of FDI innovative investment, which emphasizes creative destruction,

may overlook the scope for complementarity between FDI and domestic investment.

Assuming this hypothesis is valid, then the less technologically advanced countries may

promote the incorporation of additional more modern technologies that are also

complementary to FDI related capital and, subsequently, substitute for older, domestically

employed, technologies.

Regarding the interaction effect of foreign capital with ICT, it turns out that a

degree of substitutability characterizes the entire sample of countries. This effect is found

statistically significant in the entire panel and the sample of developing countries. If the

argument made earlier about technology imported by foreign countries not being so

technologically advanced is true, then it can be claimed that a high employment of both

FDI and ICT could lead to overinvestment and inefficiencies in the production process.

Furthermore, the interaction term between FDI accumulation and the openness

indicator is positive and significant only in developed countries. This finding indicates the

17

importance of trade liberalization for productivity gains to realize from FDI. Also, it can

possibly be argued that foreign capital is export enhancing in the case of developed

countries, while for the developing ones, foreign capital either crowds out domestic firms,

by the increase of competition, or increases imports at the expense of local producers. As,

Helpman and Krugman (1985) note, the association between outward FDI and exports in

technological leaders is mirrored by the link between imports and inward FDI in

technological followers.

These results are in line with theory which predicts that the economy with the

greater amount of human capital specializes in the production of goods and services that

cannot be produced anywhere else, so that technologically advanced economies increase

exports to FDI host countries. Similar studies show that FDI is more growth-enhancing in

countries that pursue export promotion than in those promoting import substitution

(Bhagwati, 1978), while a positive impact of outward FDI is found by Lin (1995) on both

exports of the home country to the recipient economy and imports of the host country from

the home economy.

Finally, the accumulation of FDI interacts positively with schooling only in the case

of developing countries, but this finding is insignificant. In general, as mentioned earlier,

the growth impact of schooling itself, as well as its interaction with foreign capital, was

found to be quite poor. These findings can be attributed either to measurement error or

inappropriate variable selection to capture the human capital effect. Similar evidence is

provided by Alfaro et al. (2004) showing that the interaction term of FDI with schooling is

negative. Previous findings, provided by Borensztein et al. (1998) suggest that FDI

promotes productivity growth only when the host country owns a sufficient stock of human

capital. It is likely that most of the countries under consideration have managed to hold a

18

minimum threshold stock of human capital so that an improvement of productivity is an

outcome exclusively owed to an increase of foreign capital. As it is evident from table 8,

male secondary enrollment rates display high percentages, so that FDI alone can become an

important vehicle for economic growth, holding human capital rates fixed. On the contrary,

when holding FDI fixed, a human capital increase is not a sufficient condition for a

productivity improvement.

5.5. Robustness Check

Another possible source of biased results is the case of simultaneity where a

correlation between the regressors and the error term exists. In this case, a positive

correlation between productivity and FDI is, in principle, just as likely to mean that foreign

capital is attracted to high-productivity countries as it is to mean that foreign capital raises

host country’s productivity. Furthermore, the empirical findings of Haddad and Harrison

(1993) and Aitken et al. (1997) give support to this argument. Possible variables that are

expected to be endogenous are those of foreign and domestic capital per worker, ICT

spending per worker and the interaction terms of foreign capital, as productivity shocks are

likely to affect them. To examine this hypothesis, the Arellano and Bond (1991) panel data

estimator is used, by using as instruments the lagged level of the dependent variables as

well as those of the explanatory variables mentioned above.

The first column of table 7 reports the results based on the one-step estimator, in

which the error term is assumed to be independent and homoskedastic across countries and

over time11. As it is evident, most parameters of interest retain their sign and significance

indicating that, even when controlling for the case of endogeneity, the conclusions

11 The estimates of this table are based on the entire panel of countries. An attempt was made to perform the estimator in the sub-samples of developing and developed countries, but due to the large number of lagged variables required, the model could not be safely estimated. 19

emanating from the initially described model are valid. However, as mentioned above, the

consistency of the GMM estimator is based on the validity of the instruments used in the

differenced regression (equation 6) and the absence of second order serial correlation in the

error term. As we can see from table 7, the reported Sargan test and the test which

examines for second-order serial correlation fail to reject their null hypotheses implying

that the instruments used are valid and that the error term does not exhibit second-order

serial correlation. Overall, these tests give further support to the estimated model and its

implications.

It should be noted that the error term assumption of independency and

homoskedasticity across countries and over time, is not fully realistic. For this reason,

Arellano and Bond (1991) propose a two-step estimator. This estimator results after

relaxing the assumptions of independence and homoskedasticity and constructing a

variance-covariance matrix obtained by the residuals of the first step. However, as shown

by Arellano and Bond (1991) and Blundell and Bond (1998), the asymptotic standard errors

of the two-step estimator are biased downwards, while the one-step estimator is

asymptotically inefficient relative to the two-step estimator. Consequently, the coefficient

estimates of the two-step estimator are asymptotically more efficient but the asymptotic

inference is more reliable in the case of the one-step estimator. As we can see from the

results reported in the second column of table 7, even when considering the two-step

estimator, the results do not differentiate with respect to the parameters of interest.

6. Conclusion

This paper investigates for possible innovation effects on productivity growth,

generated by the adoption of FDI, together with any impacts stemming from the

20

employment of ICT. Such effects were estimated by specifying an extended aggregate

production function and using a sample of 43 countries over the period 1993-2001. The

model was estimated by applying the fixed effect estimator for panel data and the Arellano-

Bond formula to correct for endogeneity problems.

A positive and significant impact of foreign capital is established in all groups, the

effect being larger among developing countries. Positive, yet not always significant, ICT

effects were found in the entire sample and among the developing countries. Other

interesting results include the strong substitutability between foreign and domestic capital

in the developing countries as opposed to weak complementarity observed in the developed

ones; the substitutability between foreign capital and ICT in all countries and, finally, the

positive interaction of foreign capital with openness in the developed countries. These

results provide further support to the hypothesis that FDI plays a crucial role in explaining

productivity growth in all countries, but more emphatically among the developing ones.

Possible weaknesses of this study include the, relatively, low number of years and

countries under examination, as well as the use of proxy variables for ICT and human

capital, mainly attributed to the lack of appropriate data. However, this paper is one of the

few panel data studies to compare developing and developed countries with respect to the

productivity effects stemming from the combined use of FDI and ICT. Despite the

weaknesses, the present study can stimulate future research on the above issues as more

data become available for an increased number of countries and years.

21

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25

Table 1: FDI Inflow Shares

DEVELOPING COUNTRIES DEVELOPED COUNTRIES 1990 2003 1990 2003 China 1.68 9.56 United States 23.29** 5.32 Mexico 1.27 1.92 Un. Kingdom 14.65 2.59 Malaysia 1.26 0.44 France 7.51 8.4 Thailand 1.24 0.32 Spain 6.72 4.58 Argentina 0.88 0.08 Netherlands 5.06 3.51 Indonesia 0.53 -0.10 Australia 3.91 1.41 Brazil 0.48 1.81 Belgium 3.87 5.26 Egypt 0.35 0.04 Canada 3.65 1.17 Turkey 0.33 0.10 Italy 3.08 2.93 Chile 0.32 0.53 Singapore 2.68 2.03 Philippines 0.26 0.05 Switzerland 2.64 2.17 Colombia 0.24 0.31 Hong Kong 1.58 2.42 Venezuela 0.22 0.45 Germany 1.42 2.3 Hungary 0.15 0.44 Portugal 1.26 0.17 India 0.11 0.76 Sweden 0.95 0.58 Poland 0.04 0.75 Japan 0.84 1.13 Romania 0.00 0.27 New Zealand 0.83 0.36 S. Africa -0.04 0.13 Denmark 0.54 0.46 Greece 0.48 0.01 Norway 0.48 0.42 Korea 0.38 0.67 Finland 0.38 0.49 Austria 0.31 1.22 Ireland 0.3 4.55 Israel 0.04 0.67 TOTAL 9.31 17.86 TOTAL 86.86 54.82

* The FDI inflows are calculated as a percentage of world inflows. ** Countries are sorted by descending order according to their 1990 shares.

26

Table 2: Descriptive Statistics of all variables

ENTIRE PANEL DEVELOPING COUNTRIES

DEVELOPED COUNTRIES

Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. GY 0.02 0.05 0.02 0.07 0.02 0.02 GKD 0.06 0.10 0.09 0.15 0.05 0.03 GKF 0.11 0.20 0.15 0.26 0.11 0.16 GICT 0.14 0.23 0.20 0.30 0.10 0.16 YL 10.44 0.57 9.94 0.61 10.76 0.18 SCHOOL 0.94 0.26 0.70 0.17 1.10 0.18 GOVSH 12.74 6.86 17.34 6.23 9.35 5.13 OPEN 73.01 55.09 59.84 36.40 82.69 63.89 TI 6.02 2.45 3.72 1.37 7.60 1.65 GKF*GKD 0.01 0.03 0.01 0.05 0.01 0.01 GKF*GICT 0.02 0.09 0.04 0.14 0.02 0.05 GKF*OPEN 9.20 18.66 8.61 14.03 9.58 21.09 GKF*SCHOOL 0.10 0.17 0.08 0.18 0.11 0.16 GY = Growth Rate of Output per Worker, GKD = Growth Rate of Domestic Capital per Worker, GKF = Growth Rate of Foreign Capital per Worker, GICT = Growth Rate of ICT Spending per Worker, YL = Lagged Level of ln(GDP per Worker), SCHOOL= Secondary Schooling (Male), GOVSH = Government Share of GDP, OPEN = Openness of Trade, TI = Transparency Index, GKF*GKD = Interaction term of Foreign and Domestic Capital, GKF*GICT = Interaction term of Foreign Capital and ICT, GKF*OPEN = Interaction term of Foreign Capital and Openness Indicator, GKF*SCHOOL = Interaction term of Foreign Capital and Secondary Schooling.

27

Table 3: Correlation Matrix

GY GKD GKF GICT YL SCHOOL GOVSH OPEN TI GKF* GKD

GKF* GICT

GKF* OPEN

GKF* SCHOOL

GY 1.00

GKD 0.43 1.00

GKF 0.17 0.02 1.00

GICT 0.14 0.00 0.34 1.00

YL -0.07 -0.09 0.07 -0.03 1.00

SCHOOL -0.02 -0.33 0.02 -0.04 0.50 1.00

GOVSH 0.02 0.35 0.12 0.21 -0.19 -0.52 1.00

OPEN 0.08 -0.11 -0.02 -0.04 0.00 0.04 -0.13 1.00

TI 0.03 -0.32 0.04 -0.07 0.58 0.73 -0.49 0.24 1.00

GKF*GKD -0.18 0.29 0.30 0.00 0.28 -0.16 0.30 -0.12 -0.14 1.00

GKF*GICT -0.16 -0.09 0.55 0.43 -0.09 -0.12 0.23 -0.07 -0.14 0.08 1.00

GKF*OPEN 0.08 -0.09 0.81 0.24 0.02 0.08 0.04 0.27 0.13 0.18 0.42 1.00

GKF*SCHOOL 0.14 -0.05 0.95 0.28 0.10 0.16 0.01 0.00 0.15 0.18 0.51 0.82 1.00 * See table 2 for the definitions of variables. ** The correlation matrix is calculated over a sample consisting of 235 observations which covers the entire set of variables.

28

Table 4: Entire Panel: Fixed Effect Panel Data Estimates

Dependent Variable: Growth Rate of Output per Worker

Independent Variables

(1)** (2)

(3)

(4)

(5) (6) (7)

C 1.630 (3.85)

2.256 (4.89)

1.81 (4.30)

1.904 (4.28)

2.11 (4.49)

2.214 (4.79)

1.317 (3.32)

YL -0.151 (-3.75)

-0.223 (-4.97)

-0.176 (-4.30)

-0.189 (-4.40)

-0.207 (-4.53)

-0.220 (-4.91)

-0.131 (-3.39)

GKD 0.178 (5.63)

0.166 (5.31)

0.189 (6.64)

0.136 (4.49)

0.166 (5.31)

0.168 (5.37)

0.164 (6.16)

GKF 0. 044 (2.81)

0.043 (2.31)

0.078 (4.40)

0.088 (4.35)

0.093 (2.53)

0.133 (1.85)

0.291 (4.66)

GICT 0.028 (1.78)

0.012 (0.82)

0.046 (2.99)

0.028 (1.81)

0.025 (1.57)

0.023 (1.65)

SCHOOL -0.047 (-1.07)

-0.071 (-1.59)

-0.086 (-2.15)

-0.046 (-1.10)

-0.077 (-1.74)

-0.061 (-1.37)

-0.049 (-1.29)

TI 0.011 (1.93)

0.007 (1.38)

0.013 (2.26)

0.01 (1.62)

0.011 (1.93)

0.008 (1.60)

OPEN 0.0009 (2.39)

0.0009 (2.82)

0. 0006 (1.77)

0.0009 (2.48)

0.0009 (2.55)

0.0008 (2.59)

GOVSH 0.001 (0.72)

0.0005 (0.42)

0.0005 (0.43)

0.001 (0.74)

0.001 (0.75)

0.00009 (0.08)

GKF*GKD -0.755 (-6.65)

-0.837 (-7.81)

GKF*GICT -0.271 (-4.63)

-0.270 (-5.27)

GKF*OPEN -0.0008 (-1.58)

-0.0001 (-0.23)

GKF*SCHOOL -0.097 (-1.29)

-0.172 (-2.54)

Obs. 250 235 235 235 235 235 235

R2 0.25 0.304 0 439 0.376 0.313 0.310 0. 539

F stat. 16.90 10.05 15.94 12.30 9.28 9.15 17.54 * See table 2 for the definitions of variables. ** The t-statistics are reported in parentheses.

29

Table 5: Developing Countries: Fixed Effect Panel Data Estimates

Dependent Variable: Growth Rate of Output per Worker

Independent Variables

(1)** (2)

(3)

(4)

(5)

(6) (7)

C 1.699 (2.24)

2.861 (3.73)

2.187 (3.14)

2.447 (3.24)

2.61 (3.39)

2.86 (3.71)

1.575 (2.32)

YL -0.166 (-2.21)

-0.324 (-4.12)

-0.246 (-3.41)

-0.280 (-3.62)

-0.296 (-3.72)

-0.322 (-4.07)

-0.180 (-2.57)

GKD 0.172 (3.36)

0.181 (3.88)

0.198 (4.76)

0.154 (3.34)

0.177 (3.84)

0.178 (3.75)

0.168 (4.13)

GKF 0.050 (1.55)

0.072 (1.71)

0.122 (3.12)

0.105 (2.47)

0.178 (2.24)

-0.056 (-0.27)

0.261 (1.29)

GICT 0.036 (1.25)

0.016 (0.62)

0.048 (1.71)

0.028 (1.00)

0.038 (1.31)

0.023 (0.89)

SCHOOL -0.070 (-0.76)

-0.173 (-1.87)

-0.204 (-2.49)

-0.115 (-1.25)

-0.179 (-1.96)

-0.209 (-1.90)

-0.140 (-1.53)

TI 0.048 (3.27)

0.034 (2.56)

0.05 (3.54)

0.043 (2.88)

0.047 (3.22)

0.032 (2.55)

OPEN 0.002 (2.24)

0.002 (3.02)

0.001 (1.75)

0.001 (1.95)

0.002 (2.29)

0.001 (2.08)

GOVSH 0.011 (2.74)

0.008 (2.27)

0.009 (2.28)

0.012 (2.88)

0.011 (2.64)

0.006 (1.80)

GKF*GKD -0.697 (-4.06)

-0.722 (-4.19)

GKF*GICT -0.236 (-2.41)

-0.247 (-2.82)

GKF*OPEN -0.002 (-1.57)

-0.001 (-1.17)

GKF*SCHOOL 0.186 (0.62)

-0.050 (-0.19)

Obs. 92 81 81 81 81 81 81

R2 0. 266 0.502 0.618 0.550 0.524 0.505 0.686

F stat. 6.35 6.94 9.73 7.36 6.61 6.14 9.30 * See table 2 for the definitions of variables. **The t-statistics are reported in parentheses.

30

Table 6: Developed Countries: Fixed Effect Panel Data Estimates

Dependent Variable: Growth Rate of Output per Worker Independent Variables

(1)** (2)

(3)

(4)

(5)

(6) (7)

C 1.204 (2.99)

2.124 (3.45)

2.182 (3.50)

2.163 (3.54)

2.343 (3.85)

2.279 (3.60)

2.785 (4.45)

YL -0.108 (-2.84)

-0.197 (-3.27)

-0.202 (-3.32)

-0.201 (-3.36)

-0.219 (-3.68)

-0.213 (-3.42)

-0.265 (-4.30)

GKD 0.267 (3.19)

0.327 (3.56)

0.306 (3.11)

0.301 (3.25)

0.333 (3.70)

0.342 (3.67)

0.302 (3.09)

GKF 0.032 (2.84)

0.031 (2.54)

0.022 (1.22)

0.051 (2.97)

-0.026 (-1.03)

0.126 (1.34)

0.152 (1.52)

GICT -0.013 (-0.99)

-0.012 (-0.90)

-0.0008 (-0.06)

-0.022 (-1.65)

-0.013 (-1.03)

-0.006 (-0.41)

SCHOOL -0.033 (-1.02)

-0.019 (-0.59)

-0.018 (-0.55)

-0.016 (-0.51)

-0.014 (-0.44)

-0.006 (-0.18)

0.015 (0.45)

TI -0.005 (-1.41)

-0.005 (-1.45)

-0.005 (-1.45)

-0.003 (-0.99)

-0.005 (-1.41)

-0.003 (-1.00)

OPEN 0.0009 (2.23)

0.0009 (2.26)

0.0009 (2.23)

0.0009 (2.41)

0.001 (2.43)

0.001 (2.91)

GOVSH -0.001 (-1.56)

-0.0012 (-1.59)

-0.001 (-1.51)

-0.001 (-1.50)

-0.001 (-1.55)

-0.001 (-1.46)

GKF*GKD 0.247 (0.63)

0.271 (0.71)

GKF*GICT -0.123 (-1.65)

-0.174 (-2.34)

GKF*OPEN 0.0008 (2.51)

0.0009 (3.01)

GKF*SCHOOL -0.087 (-1.02)

-0.156 (-1.81)

Obs. 158 154 154 154 154 154 154

R2 0.161 0.228 0.230 0.245 0.266 0.235 0.313

F stat. 6.23 4.47 4.00 4.33 4.85 4.09 4.44 * See table 2 for the definitions of variables.

** The t-statistics are reported in parentheses.

31

Table 7: Robustness checks: Arellano-Bond Estimates

Dependent Variable: Growth Rate of Output per Worker

Independent Variables

One-Step Results

Two-Step Results

C -0.001 (-0.38)**

-0.002 (-1.56)

YL -0.300 (-3.17)

-0.195 (-2.18)

GKD 0.204 (6.43)

0.215 (5.60)

GKF 0.302 (5.06)

0.288 (2.63)

GICT 0.043 (3.09)

0.041 (5.15)

SCHOOL 0.042 (0.83)

0.071 (1.79)

TI -0.003 (-0.62)

0.002 (0.41)

OPEN 0.001 (3.20)

0.001 (5.02)

GOVSH 0.0002 (0.21)

-0.0001 (-0.32)

GKF*GKD -1.493 (-9.92)

-1.399 (-5.39)

GKF*GICT -0.330 (-5.12)

-0.389 (-7.01)

GKF*OPEN 0.0002 (0.52)

0. 0005 (1.13)

GKF*SCHOOL -0.182 (-2.80)

-0.197 (-2.15)

Obs. 124 124 Wald stat. 373.74 11481.19 Sargan test (p-value)*** 0.370 1.000 Autocovariance test of order 2 (p-value)**** 0.249 0.389

* See table 2 for the definitions of variables. ** The z statistics are reported in parentheses.

*** The null hypothesis is that the instruments used are not correlated with the residuals. **** The null hypothesis is that the errors in the first-differenced regression exhibit no second order serial correlation.

32

Table 8: Joint Effects of FDI and Schooling

ENTIRE PANEL

SCHOOLING<0.93 SCHOOLING>0.93 FDI PER WORKER

GROWTH<0.13 0.002 0.016

FDI PER WORKER GROWTH>0.13

0.037 0.023

DEVELOPING COUNTRIES SCHOOLING<0.70 SCHOOLING>0.70

FDI PER WORKER GROWTH<0.15

0.005 -0.008

FDI PER WORKER GROWTH>0.15

0.058 0.03

DEVELOPED COUNTRIES SCHOOLING<1.10 SCHOOLING>1.10

FDI PER WORKER GROWTH<0.11

0.017 0.012

FDI PER WORKER GROWTH>0.11

0.016 0.022

* Headings of rows and columns display average FDI and schooling rates. Numbers in cells are average productivity rates of the samples belonging in each case.

33

Table 9: Comparative Analysis of Foreign Versus Domestic Capital

ENTIRE PANEL

DEVELOPING COUNTRIES

DEVELOPED COUNTRIES

SHARE OF FOREIGN CAPITAL* 0.05 0.10 0.02 SHARE OF DOMESTIC CAPITAL* 0.95 0.90 0.98 RATIO OF FOREIGN TO DOMESTIC CAPITAL 0.26 0.59 0.02 FOREIGN CAPITAL COEFFICIENT** 0.29 0.26 0.15 DOMESTIC CAPITAL COEFFICIENT** 0.16 0.17 0.30 * Shares are calculated over total capital (domestic capital + foreign capital). ** The coefficient estimates are taken from columns with the highest R2 value.

34