Sotiris K. Papaioannou * This paper investigates for possible innovation effects stemming from Foreign Direct

Transcription

1 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 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, Athens, Greece sopa@aueb.gr. **I am grateful to the Greek State s Scholarship foundation for its financial support. 1

2 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 growth 1. 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

3 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 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

4 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 microstudies. 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 , 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

5 Barthelemy and Demurger (2000), using panel data on 24 Chinese provinces in the period , 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 , 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 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 , Dewan and Kraemer (2000) 5

6 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 , 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 components 2. 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: Y it = A e ct (K it ) α (L it ) β (F it ) γ (ICT it ) δ e u it (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

7 respect to output and finally u it 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( y it ) = ln A + ct + 1 ln( kit ) + 2 ln( fit ) + 3 ln( ictit ) + uit (2) 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: lny it - lny it-1 = c + 1 (lnk it - lnk it-1 ) + 2 (lnf it - lnf it-1 ) + 3 (lnict it - lnict it-1 ) + ε it (3) 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 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 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

8 ε it = η i + α (4) it The term η i is an unobserved time-invariant country effect, while α it 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: lny it - lny it-1 = c + 0 ( lny it-1 ) + 'Χ it + η i + α it (5) where Χ it 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 η i 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, η i, 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 test 3, 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 (p-value: 0.00) when the initial hypothesis is that the difference in coefficient estimates is not systematic. 8

9 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: (lny it lny it-1 ) - (lny it-1 lny it-2 ) = 0 (lny it-1 lny it-2 ) + '(X it - X it-1 ) + (α it -α it-1 ) (6) While differencing eliminates the country specific effect, a new bias is introduced by the construction of the error term, α it α it-1 which is correlated with the lagged dependent variable, lny it-1 lny it-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[(lny it-s lny it-s-1 )*(α it α it-1 )]=0 for s 2; t= 3,...,T (7) E[X it-s (α it α it-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

10 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 secondorder serial correlation 4. 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 , 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

11 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 , to 12.8% in , as reported by UNCTAD (2003b). 6 Similar conclusions are drawn for outward FDI, since its share has increased from 5% in the period to 11.3% in

12 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 ( ). 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

13 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 (R 2 = 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 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 subsamples (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

14 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 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

15 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

16 which may not capture properly the effect of education 9. More discussion on this issue is given in the following section 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 schooling 10. 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

17 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 ( ) 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

18 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

19 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 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 time 11. 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

20 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

21 employment of ICT. Such effects were estimated by specifying an extended aggregate production function and using a sample of 43 countries over the period 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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26 Table 1: FDI Inflow Shares DEVELOPING COUNTRIES DEVELOPED COUNTRIES China United States 23.29** 5.32 Mexico Un. Kingdom Malaysia France Thailand Spain Argentina Netherlands Indonesia Australia Brazil Belgium Egypt Canada Turkey Italy Chile Singapore Philippines Switzerland Colombia Hong Kong Venezuela Germany Hungary Portugal India Sweden Poland Japan Romania New Zealand S. Africa Denmark Greece Norway Korea Finland Austria Ireland Israel TOTAL TOTAL * The FDI inflows are calculated as a percentage of world inflows. ** Countries are sorted by descending order according to their 1990 shares. 26

27 Table 2: Descriptive Statistics of all variables ENTIRE PANEL DEVELOPING COUNTRIES DEVELOPED COUNTRIES Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. GY GKD GKF GICT YL SCHOOL GOVSH OPEN TI GKF*GKD GKF*GICT GKF*OPEN GKF*SCHOOL 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

28 GY 1.00 GKD Table 3: Correlation Matrix GY GKD GKF GICT YL SCHOOL GOVSH OPEN TI GKF GICT YL SCHOOL GOVSH OPEN TI GKF*GKD GKF* GKD GKF* GICT GKF*GICT GKF* OPEN GKF*OPEN GKF* SCHOOL GKF*SCHOOL * 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

29 Table 4: Entire Panel: Fixed Effect Panel Data Estimates Independent Variables C (3.85) Dependent Variable: Growth Rate of Output per Worker (1)** (2) (3) (4) (5) (6) (7) (4.89) 1.81 (4.30) (4.28) 2.11 (4.49) (4.79) (3.32) YL (-3.75) (-4.97) (-4.30) (-4.40) (-4.53) (-4.91) (-3.39) GKD (5.63) (5.31) (6.64) (4.49) (5.31) (5.37) (6.16) GKF (2.81) (2.31) (4.40) (4.35) (2.53) (1.85) (4.66) GICT (1.78) (0.82) (2.99) (1.81) (1.57) (1.65) SCHOOL (-1.07) (-1.59) (-2.15) (-1.10) (-1.74) (-1.37) (-1.29) TI (1.93) (1.38) (2.26) 0.01 (1.62) (1.93) (1.60) OPEN (2.39) (2.82) (1.77) (2.48) (2.55) (2.59) GOVSH (0.72) (0.42) (0.43) (0.74) (0.75) (0.08) GKF*GKD (-6.65) GKF*GICT (-4.63) GKF*OPEN (-1.58) GKF*SCHOOL (-1.29) (-7.81) (-5.27) (-0.23) (-2.54) Obs R F stat * See table 2 for the definitions of variables. ** The t-statistics are reported in parentheses. 29

30 Table 5: Developing Countries: Fixed Effect Panel Data Estimates Independent Variables C (2.24) Dependent Variable: Growth Rate of Output per Worker (1)** (2) (3) (4) (5) (6) (7) (3.73) (3.14) (3.24) 2.61 (3.39) 2.86 (3.71) (2.32) YL (-2.21) (-4.12) (-3.41) (-3.62) (-3.72) (-4.07) (-2.57) GKD (3.36) (3.88) (4.76) (3.34) (3.84) (3.75) (4.13) GKF (1.55) (1.71) (3.12) (2.47) (2.24) (-0.27) (1.29) GICT (1.25) (0.62) (1.71) (1.00) (1.31) (0.89) SCHOOL (-0.76) (-1.87) (-2.49) (-1.25) (-1.96) (-1.90) (-1.53) TI (3.27) (2.56) 0.05 (3.54) (2.88) (3.22) (2.55) OPEN (2.24) (3.02) (1.75) (1.95) (2.29) (2.08) GOVSH (2.74) (2.27) (2.28) (2.88) (2.64) (1.80) GKF*GKD (-4.06) GKF*GICT (-2.41) GKF*OPEN (-1.57) GKF*SCHOOL (0.62) (-4.19) (-2.82) (-1.17) (-0.19) Obs R F stat * See table 2 for the definitions of variables. **The t-statistics are reported in parentheses. 30