Research studies

FOREIGN R&D SPILLOVERS, HUMAN CAPITAL AND TOTAL FACTOR PRODUCTIVITY

 

Prepared by the researcher  : HAMROUNI Daghbagi  – College of Economics and Administrative Sciences, Al-Imam Muhammad Ibn Saud Islamic University (IMSIU), Saudi Arabia

Democratic Arab Center

International Journal of Economic Studies : Twenty-third Issue – November 2022

A Periodical International Journal published by the “Democratic Arab Center” Germany – Berlin.

Nationales ISSN-Zentrum für Deutschland
ISSN  2569-7366
International Journal of Economic Studies

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Abstract

Foreign R&D spillovers depend strongly on the level of human capital in host countries. In this paper, we empirically study the direct and indirect effects of human capital on the magnitude of foreign R&D spillovers and on economic growth. To do so, we use panel data from a sample of 42 countries over the period 2000-2019. This sample is divided into four regional subgroups to analyze the regional effects of foreign R&D spillovers. Our main results are: i) the diffusion of foreign R&D is an important factor of economic growth, ii) human capital plays an important catalytic role in internalizing foreign R&D spillovers. Spatial interactions of human skills are an important source of ideas and information flows and positive externalities and iii) countries with higher levels of human capital benefit more from foreign R&D spillovers conveyed through international trade.

  1. Introduction.

Openness plays an important role in the exchange of ideas and knowledge between different countries and has a positive effect on their different productivities because it allows rapid access to foreign technologies. These effects depend on the extent of diffusion and the mechanisms by which knowledge and technologies are transmitted. The extent of spillovers depends on human capital, which plays a direct role as a factor of production and an indirect role as a catalyst for spillovers. This is because human capital facilitates the understanding of the technical configurations of imported equipment and machinery. In addition, it enables the internalization of foreign R&D spillovers and spillovers from FDI activities. Our objective in this paper is to study the effects of knowledge diffusion on the economic growth of recipient countries and to test the role of human capital in the diffusion of foreign R&D spillovers. First, we test the interaction effects between foreign R&D spillovers and human capital on economic growth for a set of developing countries. Secondly, we test the same effects but in a regional context. Thus, we empirically investigate the direct and indirect effects of human capital on foreign R&D spillovers and on economic growth. To do so, we organize the rest of our paper as follows: Section 2 presents a review of the literature on the role of human capital in foreign technology internalization and FDI spillovers. In Section 3, we define the variables of interest and describe our empirical methodology by presenting the model to be estimated, the different variables of interest and dataset. The estimates, results and comments are presented in Section 4. Section 5 concludes, summarizes the results and provides policy recommendations.

  1. Literature Review

R&D benefits not only countries that invest heavily in research activities but also other foreign countries through cross-border knowledge flows. The effects of foreign R&D depend on several factors and in particular on the level of human capital. The role that human capital plays in the benefit of foreign R&D as a source of economic growth has received much attention in the economics literature. Indeed, several theoretical and empirical models have shown that human capital is an important factor for productivity and economic growth. It helps to understand the configurations of new technologies adopted and facilitates the international transmission of knowledge between different countries. Human capital also helps to assimilate imported technologies and is an important catalyst for spillovers. Imported technologies can only play their role in spillover transmission and economic growth when the level of human capital is sufficient. In terms of knowledge diffusion, human capital is important because it diversifies the channels for transmitting new technological knowledge and promotes communication between different actors. Acquiring information is easier and less costly for people with a considerable level of human capital. Highly educated people only need to leaf through catalogs, briefs, inventories, manuals, etc. to get the refined and necessary information. Some studies that have analyzed the relationship between the knowledge diffusion and human capital have shown that the latter facilitates, in a dynamic process, the subsequent adoptions of new technologies. Moreover, human capital promotes economic growth through the learning process, especially in export-oriented manufacturing industries. This effect is clearly explained by models of endogenous growth theory.

      Economists identify two types of relationships between human capital and technology: there are those who use the term “skill in adoption” to say that human capital is a key factor in the diffusion and adoption of new technologies. Others use the term “skill in use” to mean that certain technologies are complementary to human capital. Comparing these two types of relationships between human capital and technology diffusion, we can say that in the first relationship, the diffusion of any new technology depends on human capital. In the second, only the diffusion of some technologies requires human capital. In discussing the effect of human capital on knowledge diffusion, a distinction must be made between technical and general education. Technical education is considered more important than general education in promoting the diffusion process and assimilating foreign technologies. Moreover, technical education should have a much stronger and more direct effect on production than general education. However, this does not diminish the role of general education, which remains an important factor in the overall production process. There is no doubt that a better general education of the population allows a better assimilation of new technologies and strengthens technological capacity in the country.  Some works, have shown that the convergence process is a conditional outcome of technological diffusion. The catching-up process is faster when the level of human capital is higher. In our recent paper in International Review of Economics and Finance (Hamrouni, 2022), we developed two theoretical models to study the effects of the international diffusion of knowledge on differences in labor productivity and catching up between northern and southern countries. In particular, we found that catching up is an endogenous process and that its speed depends negatively on the rate of knowledge accumulation in the North and positively on the investment in human capital in the South. Moreover, the difference in average labor productivity between Northern and Southern countries depends on the differences in the rate of knowledge accumulation between them (innovation in the North and imitation in the South).

 Human capital is important because it also enables the internalization of FDI spillovers, which represent the second channel of knowledge diffusion after the import of foreign technologies. Indeed, generally, FDI typically provides significant training to its managers and workers through training or learning-by-doing schemes. Human capital helps domestic firms to understand the technologies adopted by foreign firms and thus facilitates imitation and learning processes. Indeed, in the presence of FDI, externalities will arise if skilled workers move from foreign to domestic firms. Moreover, domestic firms can improve their productivity even by observing foreign firms. This “Learning-by-Watching” effect represents a form of direct imitation that allows domestic firms to benefit from new products, new technologies, new techniques and new production and marketing methods introduced into the domestic market by FDI. These positive externalities, which represent a contagion effect, are important if domestic firms use technologies comparable to those used by foreign firms and if the level of human capital in the host countries is relatively high to allow transmission of these spillovers. On the other hand, if the level of learning of foreign firms is low, spillovers are also lower. In other words, the spillovers resulting from imitation or observational learning processes depend on the level of competence of domestic firms in assimilating information flows Thus, it becomes essential to intensify the relationship between foreign and domestic firms to promote technology diffusion and spillovers. Moreover, if spillovers are a result of cooperative practices and strategies between foreign and domestic firms, then the latter can only access cooperative arrangements if they have highly skilled workers. Moreover, if the spillovers depend on the volume of FDI, then human capital, which is a factor in attracting FDI, has a positive and indirect effect on spillovers.

The importance of human capital to internalize spillovers from FDI is confirmed by several empirical studies. Indeed, Wang (1990) developed a model to study the interaction between human capital and FDI. Wang concluded that FDI is directed towards countries with higher levels of human capital and more developed innovation systems.  Moreover, FDI represents an important source of improving the quality of human capital (learning) in host countries. Su and Liu (2016) used panel data to study the determinants of economic growth in Chinese cities over the period 1991-2010.  In particular, they focus on the interaction between FDI and human to affect the economic growth of Chinese cities. Among the results of this study, the effects of FDI on the economic growth of Chinese cities are intensified by the city’s human capital endowment. Moreover, the complementary effect of FDI on human capital is higher for technology-intensive FDI than for labor-intensive FDI. This implies that human capital facilitates technology transfers resulting from FDI. Similarly. Blomstrom and Wang (1992) studied the effect of FDI on economic growth using cross-sectional data from a sample of 78 developing countries They use human capital as an internalizing factor of FDI spillovers. The results show that the positive effect of FDI on economic growth is conditioned by the level of human capital in host countries. FDI spillovers only exist above a threshold level of human capital.  Borensztein, De Greguerio and Lee (1998) studied the effects of the interaction between FDI and human capital on technology diffusion and economic growth in 69 developing countries during the 1970s and 1980s. The main results of this study are:  FDI contributes to economic growth only when the host country has sufficient capacity to absorb advanced technologies. Specifically, FDI can play an important role for growth if the level of human capital is relatively high FDI has a positive effect only in countries with a secondary school enrollment rate above 0.45. This implies a complementary relationship between FDI and human capital in the promotion of economic growth. FDI has a positive and significant effect on domestic investment, which implies a complementary rather than a substitution effect between the two types of investment. The lack of spillovers can be explained by several factors such as the scarcity of skilled labor, low mobility in the labor market, and the weak incentives for multinationals to diffuse their knowledge to local firms. Berthélemy and Démurger (2000) estimated the interaction effects between FDI and human capital with a simultaneous equation model using panel data for 24 Chinese provinces during the period 1985-1996 The results of this study proved a positive and significant interaction effect between human capital and FDI.

  1. Empirical methodology

Building on previous works, our objective in this paper is to study the interaction effects between human capital and foreign R&D spillovers on economic growth. For this purpose, we use panel for a sample of 42 countries during the period 2000-2019. The estimated model is as follows:

(1)

For each country i, is the total factor productivity. The variables ,  and  measure respectively the stock of foreign R&D, human capital and the degree of openness. All these variables are expressed in logarithm. The variable  measures the interaction effect between human capital and foreign R&D. Thus, human capital is used in two different ways: first, as a determinant of TFP, and second, as a catalyst for spillovers. In the latter case, we are interested in the interaction effects between human capital and foreign R&D to see to what extent spillovers from foreign R&D are influenced by the level of human capital. These interaction variables are interpreted as follows: if the estimated coefficient of the interactive variable is positive and significant, then the effect of foreign R&D is larger if human capital is high. On the other hand, the effect of human capital larger if the stock of foreign R&D is high.

  1. a) Total Factors Productivity

TFP is measured by the growth accounting method in which TFP is synonymous with technical progress. In other words, TFP is the part of growth not explained by the physical quantities of two traditional factors (capital and labor). For each country, TFP is calculated from a Cobb-Douglas production function with constant returns to scale as follows:

 (2)k of physical capital and the employed population respectively. Since the contribution of TFP depends on the elasticity of production to physical capital, we calculated TFP by assuming a value of 0.4 of   that is often used in empirical studies (Coe, Helpman and Hoffmaister, 1995).

  1. b) Foreign R&D stock

The stock of foreign R&D measures the technological diffusion of advanced countries, which has an effect on the economic growth of host countries. This effect can be summarized as follows: If R&D generates different new intermediate goods (horizontal differentiation) and of different qualities (vertical differentiation) and if these intermediate goods are exported to other countries, then the total factor productivity of importing countries increases. The use of new goods creates new opportunities in several areas of production and allows the creation of new productive activities. To calculate the stock of foreign R&D, we adopted the method adopted in other empirical studies. For each country in our sample, the foreign R&D stock is the sum of the domestic R&D stocks of the seven advanced countries, weighted by the shares of bilateral trade with each country. It is therefore essential to first calculate the domestic R&D stocks of advanced countries. The method used is the perpetual inventory method. Thus, the R&D stock in year t is equal to its value in t-1, corrected by a depreciation rate, to which is added the investment in year t, measured by domestic R&D expenditure.

 (3)

Where is the R&D investment in year t and is its depreciation rate. Following Coe and Helpman (1995), we have chosen a value of 5% for this rate. The initial R&D stock is equal to the initial investment divided by the sum of the annual growth rate of investment in R&D, ( ) and the R&D depreciation rate, ( ).

(4)

Data on R&D expenditure at constant prices and PPP $ are taken from the OECD database (science and technology indicators). For advanced countries (spillover sources) we calculated average annual growth rates of R&D investment between 2000 and 2019.

Table 1: Average annual growth rate of R&D investment in advanced

               countries (2000-2019)

country rate % country rate %
Canada 1.15 USA 2.82
France 1.55 UK  2.13
Germany 2.74 Japan 1.36
Italy 2.22    
Source: Author’s calculations based on OECD database (Science and Technology Indicators)

This table shows that over the period 2000-2019, the domestic R&D stock increased in these seven advanced countries. However, this increase varies from country to country. The USA and Germany show the most important evolution. From the R&D stocks of these advanced countries, we can determine the variable representing the diffusion of foreign R&D to other countries following the approach adopted by Coe and Helpman (1995) who use the following measure: (5)

Where is the stock of foreign R&D going to the country  at time .  represents the imports of country  from the advanced country  at time (t).  indicates the total imports of country  at the time . The foreign R&D stock is calculated using the CHELEM (2019) database, which describes bilateral trade relationships between different countries.

  1. c) Human Capital

Empirically, several indicators are used to measure human capital accumulation, such as the secondary school enrollment rate, the average number of years schooling, the average level of education, educational expenditures that reflect the investment in human capital, and the ratio of intellectual to manual labor. Engelbrecht (1997) finds that the introduction of human capital, measured by the average number of years of education of the population over 25 years of age, reduces the estimated coefficients of domestic and foreign R&D. Similarly, Tomas Barrio-Castro and al. (2002) re-examined the relationship between human capital, domestic and foreign R&D and TFP for 21 OECD countries over the period 1966-1995. Their results show that using average years of schooling instead of the secondary school enrollment rate affects the magnitude of the effect of R&D spillovers on productivity: : the coefficients resulting from both domestic and foreign R&D effects decrease. In some cases, there is a decomposition according to the level of primary, secondary and tertiary education. The objective of this decomposition is to see which level of education has the most impact on economic growth in interaction with other variables (FDI, foreign R&D, openness). Berthélemy and Démurger (2000) use the ratio between the annual number of graduates of different levels of education (primary and secondary) and the total population to construct a measure of human capital. This number is adjusted by the mortality rate to account for the depreciation of knowledge. In this paper, we measured human capital (H) by the secondary school enrollment rate. The data are taken from the World Bank’s WDI database (2020).

  1. d) The degree of openness

Openness has a positive and significant effect on productivity and economic growth. There are several reasons for this, such as access to a large number of intermediate goods incorporating new foreign knowledge, the possibility of communication with the outside world, which facilitates the absorption and imitation of foreign technologies, and economic and technological catching up. Openness boosts the learning process and allows for a more efficient allocation of resources, etc. In this paper, we have measured the degree of openness by the import rate (imports / GDP). This indicator, which is often used in empirical studies to measure the effects of foreign R&D spillovers is more consistent with the idea that spillovers spread through imports.  In reality, there are other measures of the degree of trade restrictions, such as tariffs, non-tariff barriers (import quotas and the like), relative prices of tradable and non-tradable goods, export to GDP ratio, etc.

  1. Estimates and results

In this section we use equation (1) to estimate the interaction effects between human capital and foreign R&D on TFP growth using panel data. Our approach follows three steps: first, we estimate the spillovers and interaction effects for the whole sample. In this case, all countries (including the seven advanced countries) are assumed to spillover recipients. Then, we estimate spillovers and interaction effects without taking into account the advanced countries. Otherwise, we are interested in North / South spillovers. Finally, to account for regional disparities in spillovers, we split the sample into four continental subgroups. The first subgroup contains the American countries, the second the European countries, the third the Asian countries and the last the African countries.  In our estimations and on the basis of the Hausman test, the fixed effects model has been preferred over the random effects model.

  1. a) The whole sample

For the whole sample, the results (Table 2) show that the diffusion of R&D spillovers between countries has a positive effect on TFP. Indeed, the coefficient of the foreign R&D stock is positive and significant. These results are consistent with those of Coe and Helpman (1995) who proposed a simple linear model to study the effects of domestic and foreign R&D on TFP growth in developed countries. Their results prove positive and significant effects of domestic and foreign R&D stock on TFP. Other studies that focused on the impact of foreign R&D on economic growth show that it is not only domestic R&D that plays an important role in TFP growth for developed countries, but also foreign R&D. Some studies show that the effects of domestic and foreign R&D on TFP are less significant in developing countries than in developed countries.  This can be explained by reasons such as the low level of domestic R&D in developing countries. In addition, the low level of human capital and local R&D as well as political and economic instability in developing countries generate low returns to R&D and this reduces the spillovers effects on TFP.  In a study on a sample of 70 developed and developing countries, Goñi and Maloney (2017) showed that the effect of domestic R&D on TFP growth is greater in middle-income countries than in low- or high-income countries.

Table 2: Foreign R&D Spillovers and human capital: the case of whole sample

Dependent variable: Log  of TFP
Independent variables (1) (2)
Constant -1.628

(-3.69)

-1.367

(-2.79)

LFRD 0.126

(6.88)

0.1168

(5.74)

LH 0.0615

(3.18)

 
LFRD*H   0.00222

(3.17)

Lopen 0.1089

(6.62)

0.1089

(6.62)

0.29 0.28
N 831 831
The values between brackets are Student’s t

Dierk Herzer (2022) studied and compared the effects of domestic and foreign R&D on TFP using panel data for a large sample of 82 developing countries (49 middle-income countries and 33 low-income countries). The results show that in developing countries, domestic R&D has a larger effect on TFP than foreign R&D.  In addition, domestic R&D has a positive effect on TFP, but this effect is larger in middle-income countries than in low-income countries. Openness also has a positive and significant effect on TFP growth. This justifies the interest of less restrictive trade policies to benefit from the technological progress of the leading countries. The results also show that human capital as a factor of production plays an important role in TFP growth. Indeed, the coefficient of human capital is positive and significant. In addition, the coefficient of the interaction variable between human capital and foreign R&D is positive and significant. This result implies that foreign R&D spillovers are higher when the level of human capital is higher. Thus, in addition to its direct effect on TFP, human capital also has an indirect effect because it facilitates the absorption and adoption of foreign technologies and reduces the costs of their imitation. These two effects of human capital are widely discussed in several endogenous growth models. Human capital has a direct effect because it allows the creation of local innovations and an indirect effect because it represents a catalyst for catching up between countries. Coe, Helpman and Hoffmaister (1995) have shown empirically that the contribution of foreign R&D depends on the level of human capital in recipient countries and the contribution of foreign R&D to economic growth is conditioned by the level of human capital.

  1. b) Spillovers between advanced countries and other countries.

Since advanced countries are leaders in innovation and R&D and developing countries are followers, most of the international spillovers flow from advanced to developing countries. It is therefore more relevant to estimate the spillovers between advanced (North) and developing (South) countries. To do this, we estimate spillover effects without taking into account the seven advanced countries and we use only the other 35 countries. The results (Table 3) confirm the hypothesis that international trade plays an important role in the diffusion of technology from Northern to Southern countries. Indeed, the coefficient of foreign R&D is positive and significant. Human capital also plays the expected role, its coefficient is positive and significant. Regarding the interaction effects between foreign R&D and human capital, the results show that the coefficient of the interactive variables is positive and significant. This means that countries gain more from foreign R&D activities if they have a high level of human capital. This result justifies the interest of educational policies more oriented towards scientific and technological fields.

Table 3 : Foreign R&D Spillovers between advanced countries and other countries.

Dependent variable: Log  of TFP
Independent variables (1) (2)
Constant  (-2.92) -1.248

(-2.18)

LFRD 0.1149

(5.36)

0.1054

(4.45)

LH

 

0.0635

(2.95)

 
lFRD*H

 

  0.0023

(3.17)

Lopen 0.1233

(6.60)

0.1232

(6.60)

0.27 0.27
N 700 700
The values between brackets are Student’s t

  1. c) Regional spillovers.

In reality, although foreign R&D spillovers have positive effects on TFP growth, they cannot have the same effects in all countries or in regions. To account for differences in spillover effects across regions, we split the overall sample into four continental subgroups. The first group contains the American countries, the second the European countries, the third the Asian countries, and the last the African countries in the sample.

i)The European countries group

For the European continent (Table 4), the results confirm a positive and significant effect of international trade on TFP growth. Indeed, the coefficients of foreign R&D and openness are always positive and significant. Human capital also plays an important role. Indeed, the direct and indirect coefficients (the interactive variable) of this factor are positive and significant. This positive effect of human capital is explained by the scientific and technical content of European education systems. According to international statistics (UNESCO, UNCTAD), European countries have invested more in human capital than Latin American and African countries. The positive effect of openness is not surprising, since most European countries have long adopted strategies of openness and export promotion. Moreover, the European Union and intra-European relations reflect the high degree of openness of these countries.

Table 4: Foreign R&D Spillovers and human capital: case of European countries group

Dependent variable : Log  of TFP
Independent variables (1) (2)
Constant -3.925

(-7.58)

-3.607

(-6.13)

LFRD 0.250

(11.44)

0.238

(9.71)

LH

 

0.068

(2.82)

 
LFRD*H

 

  0.0024

(2.83)

Lopen 0.2773

(4.35)

0.2774

(4.35)

0.55 0.55
N 331 331
The values between brackets are Student’s t

  1. ii) The African countries group

For African countries, we find the expected effects of foreign R&D spillovers and openness on TFP growth. Indeed, Table 5 shows that the coefficients of the FRD variable and openness are positive and significant. However, compared to previous estimates, human capital does not seem to have a positive effect on TFP. Indeed, its coefficient is not significant.  We also note that the interaction variable between human capital and foreign R&D does not confirm the existence of positive and significant effects on TFP in this group of African countries. This result is in line with other empirical studies which have found non-significant and sometimes negative effects of human capital on economic growth. Indeed, Pritchett (1996) estimated a Gobb-Douglas production function augmented by human capital for a sample of developing countries during the period 1960-1985. He found a negative correlation between education and the average annual growth rate of GDP per capita. This result can be explained by several reasons such as the low level and / or quality of human capital and the failure of education and training systems, especially in developing countries. In addition, the results of investment in human capital do not appear until later. Measures of human capital may also explain the unexpected results of the effects of this factor.

Table 5: Foreign R&D Spillovers and human capital: case of African countries group.

Dependent variable : Log  of TFP
Independent variables (1) (2)
Constant -0.130

(-0.67)

-0.166

(-0.88)

LFRD 0.087

(4.09)

0.088

(2.90)

LH -0.012

(-0.42)

 
LFRD*H   -0.0007

(-0.18)

Lopen 0.1064

(3.21)

0.1065

(3.21)

0.23 0.23
N 100 100
The values between brackets are Student’s t

The secondary school enrollment rate does not reflect the quality of education, since it is determined without taking into account the number of days in each grade or the number of years per cycle. Similarly, it does not take into account the quality of the education acquired (quality of teachers, number of students per class, etc.). Finally, the secondary school enrollment rate, which is based on flows to calculate stocks, suffers from knowledge of the initial value. To explain the insignificant effect of human capital on TFP that we found for the subgroup of African countries, we can say that this group includes Algeria, Morocco, Tunisia, Egypt and Nigeria. However, the Arab countries have a significant deficit in terms of education and knowledge production and suffer from insufficient resources to finance research and development activities. Not to mention the brain drain to advanced countries, which considerably hampers the local absorption capacity of the Arab countries. This brain drain is due to the lack of attractive job opportunities and the less motivating socio-political environment in the Arab world. Finally, overdependence on oil is one of the reasons that hinder innovation and knowledge production in the Arab world. Oil wealth implies a rent that often encourages idle spending and does not encourage research. Hamid Mohtadi (1998) analyzed the effect of human capital on spillover transmission using a sample of eleven MENA countries. The results of this study show that human capital did not play the expected role in knowledge transmission in MENA countries. The author explains this result by the following reasons: i) these countries are generally less endowed with human capital, which cannot fully play its role in technology transfer to these countries. ii) Protectionist measures adopted by these countries reduced the role of technology transfer and discouraged domestic investors from adopting new technologies. iii) Arab countries do not have appropriate institutions to internalize the spillovers of FDI.

iii) The Asian countries group

For the subgroup of Asian countries, the coefficient of foreign R&D is positive and significant and prove that international trade plays an important role in the transmission of spillovers to this region. The results also show that the openness in these countries promotes growth, its coefficient is positive and significant. These results reflect the opening-up strategies and reforms implemented by these countries to promote economic growth. For example, since 1994, China has become the largest recipient of FDI of all developing countries and the second largest in the world after the United States. This advantage is due to economic conditions, labor market characteristics, and China’s size in terms of space, population, and GDP.

The coefficient of human capital is positive and significant. This result is not surprising given the human capital investment policies adopted by Asian countries since the 1960s to promote economic growth. These policies have created a favorable environment for internalizing the spillovers of foreign R&D. Investment in education and R&D has enabled the Asian dragons to achieve spectacular experience in recent decades. The interaction variable between R&D and human capital has a positive and significant coefficient, confirming the catalytic role of human capital in these countries.

Tableau 6: Foreign R&D Spillovers and human capital: the case of Asian countries group

Dependent variable : Log  of TFP
Independent variables (1) (2)
Constant -2.66

(-2.51)

-2.18

(-1.84)

LFRD 0.126

(6.88)

0.094

(1.93)

LH 0.147

(3.37)

 
LFRD*H   0.0050

(3.21)

Lopen 0.188

(5.54)

0.188

(5.52)

0.50 0.50
N 200 200
The values between brackets are Student’s t

 

  1. iv) The American countries group

As for the other regions, the results (Table 7) show that international trade plays an important role in knowledge transmission to the group of American countries. However, unlike the Asian and European countries, the coefficient of human capital is positive but not significant. Moreover, human capital does not seem to play any interactive effect with foreign R&D.

Table 7: Foreign R&D Spillovers and human capital: case of American countries group

Dependent variable: Log of TFP
Independent variables (1) (2)
Constant -0.42

(-2.17)

-0.032

(-3.31)

LFRD 0.146

(13.70)

0.084

(1.97)

LH

 

0.0002

(0.93)

 
LFRD*H   0.0010

(1.48)

Lopen 0.0008

(1.59)

0.0011

(2.23)

0.53 0.54
N 190 190
The values between brackets are Student’s t

 

  1. Conclusion

In this paper we studied the interaction effects between human capital and foreign R&D spillovers on economic growth using a sample of 42 countries. The main findings and recommendations are as follows:

For all countries, foreign R&D is an important source of economic growth. It is therefore important for poor countries to keep up with technological developments in advanced countries in order to promote their economic growth. The positive effects of openness show that countries that are more open to foreign technology imports and attract more FDI benefit more from international technology diffusion. It is therefore important to adopt open strategies and avoid any form of protection in order to benefit from foreign R&D.

Human capital is an important catalyst for spillovers. In terms of economic policies, our results suggest that investment in human capital and improved education levels are essential to benefit from foreign R&D spillovers and to promote economic growth. Thus, the diffusion of foreign technology requires significant investments in human capital such as the creation of specialized centers for training and scientific, technical and technological information that is crucial for the speed of diffusion of new knowledge. It is also necessary to invest in all factors that increase the local absorption capacity, such as public infrastructure and institutional environment. The degree of development of these factors determines a country’s ability to benefit from technological change and to catch up with advanced countries.

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