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Globalisation, human capital and technological catch-up

Suma Athreye,Economics, Open University, UK.

John Cantwell,Rutgers Business School, USA.

Paper prepared for the ESRC research seminar series on International trade,

Technological Change and Labour, 5-6 April 2005

Preliminary draft: numbers may change. Please do not cite or quote without

authors permission

Abstract:

The interest in this paper is to observe over a long span of time (1950-2001) the

periods of technological catch-up in the sense of new countries contributing to technology

generation in the world economy. We also assess the role of globalisation (through trade,

and inward FDI) and human capital in explaining such technological catch-up. Our

empirical analysis shows that 1950-65 and 1992-2001, were periods of significant

technological catch-up in the world economy. However, despite the catch-up of the Four

dragons, the decades of the 1970s and 1980s were periods of overall technological

concentration when increases in world technology generating capacity came from a small

group of countries that had already begun with significant patent shares. We also find that

trade and inward FDI encouraged catch-up while the increasing concentration of the

worlds human capital tended to increase technological concentration.

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Globalisation, human capital and technological catch-up

There is considerable debate on the issue of whether new countries in the

developing world are catching-up in technological capabilities and if they can emerge as

significant producers of technology. On one-hand countries like Ireland, Israel and India

have emerged as significant exporters of technologically sophisticated products and

significant multinational R&D in these sectors has moved to these countries. On the other

hand, there is evidence that some countries from Sub-Saharan Africa have shown

technological regress in recent years.

The issue of whether new countries are themselves emerging as generators of

technology is of importance for two kinds of reasons. The first reason, as an empirical

literature on twin peaks and convergence clubs has argued (Quah 1996), is because the

heterogeneity in technological diffusion ultimately determines the evolution of the world

distribution of incomes. However, secondly, the participation of new countries in the

production of technology is also an issue of interest in its own right from the perspective of

the provision of their own development needs. For one thing, there is concern about the

ability of developing countries to develop environmentally friendly technologies and drugs

to combat diseases that disproportionately affect poor country populations like AIDS and

Malaria. Can developing countries produce technologies appropriate for their needs? The

answer to this question could dictate fundamentally the policies that should be adopted. If

technological catch-up is slow it may be socially more useful to find ways to subsidise the

production of such technologies in developed countries. For another thing, the capacity of

developing countries to participate in the higher value creating parts of global production

networks, and thus to catch-up economically with established industrialised countries, both

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depends upon and is reflected in the emergence of their own indigenous technological

efforts.

Our paper is more concerned with the second reason for studying technological

catch-up in the second sense than the first. The interest in this paper is twofold:

-To observe over a long span of time (1950-2001) the periods of technological

catch-up in the sense of new countries contributing to technological generation.

- Explain the relative importance of globalisation and human capital in influencing

technological catch-up in the world economy

We measure a nations contribution to technology generating capacity by observing

the patent shares attributable to the nation in all patents granted in the US. As more

countries start contributing to the worlds technological capacity we should observe a

relative dispersion of the origin of patents across the world in the dataset. We employ a

particular decomposition of the Herfindahl index of patent concentration that allows us to

track the aggregate influence of new patentees in the overall dispersion of patents. This

decomposition demarcates more clearly the characteristics of periods of technological

catch-up and periods of technological concentration.

We then use time series techniques to explain the movement of this catch-up term

due to globalisation and human capital build-up in the world economy. We pay attention to

different dimensions of globalisation in the world economy - openness to trade, share of

international production, growth of international patenting and the growth a and variance in

the stock of human capital.

Our empirical analysis shows that 1950-65 and 1992-2001 were periods of

significant technological catch-up. Despite the catch-up of the Four dragons, the decades of

the 1970s and 1980s were periods of overall technological concentration when increases in

world technology generating capacity came from a small group of countries that had

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already begun with significant patent shares. In assessing the role of globalisation we find

that openness to trade and inward FDI played an important role in explaining technological

catch-up. However, increased international patenting by multinationals and the increasing

variance of the human capital encouraged the concentration of patents among a few

countries.

The remainder of the paper is organised as follows: A brief review of the literature

on technological catch-up in Section 1, is followed in Section 2 by an outline of the method

employed in our study, including a description of the method used to track technological

catch-up in the world economy. Section 3 describes our main results and Section 4

concludes.

1. A brief review of the literature on technological catch-up

The literature on technological catch-up has developed according to two rather

different traditions. The first is the growth accounting inspired studies of convergence at a

global macro level, and the second has been an almost parallel literature on technology and

development based on the more detailed study of historical episodes and successful

development of technology in new regions of the world. Very good reviews of both

literatures exist and our aim here is to stress the important and complementary conclusions

to which the two literatures come, albeit from different starting points.

In the growth accounting tradition, the rate of growth of technology is seen as the

ultimate factor constraining the long-term economic growth of nations. Early studies in

this tradition introduced the not so intuitive idea that the larger the technological gap the

faster would be the potential catch-up as countries converged to the same level of income

due to the free availability of technology through trade. They also provided considerable

support for the view that the G-8 countries had converged to US levels of income in the

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post-war period. This convergence was aided by capital flows from the US to Europe and a

favourable trade and aid regime from 1950-65.

However, the view that this convergence in world incomes was general and

extended to all countries soon ran into the sand as it was clear that world incomes were not

converging to any one level. Instead it was pointed out that there were convergence clubs

of high and low-income countries. These twin peaks in the world distribution of income

(Quah 1996) and membership of countries in either the richer or poorer club ultimately

hinged upon the heterogeneity/differences of technology across countries (Bernard and

Jones 1996). In a second development two seminal papers by Romer also introduced the

idea of cumulative causation in growth because of the public good aspect of technology

and the effects of learning on productivity. The important departure of these papers was to

amend neoclassical growth models to make the generation of technology endogenous to the

processes of investment and economic growth, and this provided a perspective on why such

twin peaks might exist at all.

In contrast to the growth accounting models, the literature on technology and

development had always recognised the essential heterogeneity of the technological catch-

up process as well as its endogenous character. Drawing on industrial history these

scholars offered mixed answers to the narrower question of whether new countries could

catch-up and become generators of new technology.1 Historically, each fresh wave of

technological change did see some new countries catch-up technologically by exploiting

the new opportunities that occasionally emerged in the technological transitions between

waves. Outstanding examples of such a success were the cases of German industrialisation

in the late nineteenth century and later the catch-up of the US. Yet these countries made

significant complementary investments in infrastructure and developed unique institutions

1For a survey of this literature see Athreye and Simonetti (2004).

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that facilitated innovation and growth. Firms in these economies had also developed

unique strategies to meet the incumbent competition and exploit the opportunities provided

by the newly emerging electricity technology. Examples include the early R&D facilities

of German chemical firms and their close links with the university sector, and the invention

of joint stock companies in the US to pool financial risks.

The literature on technology and development ascribed the heterogeneity of

technological experiences of countries to two main factors: differences in the technological

capabilities of the firms of nations (see Bell and Pavitt 1997) and differences in the

institutional structures governing innovation by firms and linking them with a variety of

other actors in the economy, which is also sometimes referred to under the heading of

National Systems of Innovation (see Freeman 1997 and a somewhat different take by

Lundvall 1992 ).

Despite significant differences in their conception of technology and the role of

institutions in technological catch-up, both traditions share the importance they ascribe to

human capital and globalisation in the technological catch-up process. The post-war

convergence of incomes and technological catch-up involved a number of countries that

had had strong historical links through the migration of people. In a recent work ORourke

and Williamson (19XX) have shown that a large part of the catch-up of European wages to

US levels in the inter-war period was explained by migration and to a lesser extent by

capital flows. The post-war catch-up seemed to reverse this older trend with capital flows

and trade playing a more important role than migration. The technological catch-up of

Japan in the mid-1970s, and the Four Dragons in the late-1980s was also closely associated

with a globalisation of trade and production in the world.

Another important factor emphasised in both literatures is the role of human capital

and training to the technological catch-up process. Studies on the emergence of new

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science based regions such as those by Bresnahan and Gambardella (2004) and Arora and

Gambardella (2005) also suggest that human capital variations have opened up the

possibility for new regions and nations to occupy distinctive technological niches in a

global market based upon variations in their stock of human capital. Recent examples of

technological catch-up such as those of Israel and Taiwan point to the important role of

openness and human capital investment in creating distinctive comparative advantage

positions for the countries often in global production chains.2

However, the influences of the two dimensions of globalisation and human capital

on catch-up need more careful empirical study. It is well recognised in the literature on

international trade, foreign investment, human capital and economic growth that there is

considerable interrelation between the three and so disentangling their influence on growth

is very problematic. In this paper we construct a measure of technological catch-up that

does not depend upon growth measures in a direct way. This allows us to bypass some of

the endogeneity issues and examine the impact of the three factors on technological catch-

up and assess the direction of causality.

2. Methodology employed

2.1 Measuring technological catch-up

This paper uses a USPTO patent database to construct an index of technological

catch-up in the world economy. The USPTO database has advantages and disadvantages in

the analysis of technological behaviour and these have been widely discussed in the

literature using patent data.3 For our purposes a major advantage is that it helps us track

2These case studies also emphasise the large and coordinated investments by numerous agents in theeconomy required to achieve success in technological catch-up and the role of indigenous institutionsin imparting unique advantages to nations. It is beyond the scope of the aggregated level of analysis of

this paper to examine these aspects of technological catch-up, though we think such factors do affectthe inter-country differences in catch-up.3See e.g. Schmookler, 1950, 1966, Pavitt, 1985, 1988; Griliches, 1990; Archibugi, 1992.

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contributions of countries to the world technology generating capacity directly. This

collective innovative capacity is sometimes viewed as representing what is contemporarily

a common world technology frontier, but we follow the modern evolutionary perspective in

supposing that technology is instead developed in an incremental, localised and

differentiated fashion at multiple different sites and following multiple different paths or

approaches to innovation. The US Patent share of countries thus represents an

underestimate of the true technological capacity of countries.

The number of foreign (non-US) countries actively patenting in the dataset rose

slowly from 42 in 1950 to a high of 60 in 1989, although not every country patented every

year. This is far fewer than the total number of countries we were able to collect economic

data for from sources like the Penn Tables and the World Development Indicators. Thus,

like with firms, only a very small proportion of countries patent and demonstrate

technological capabilities. To check on how good a measure of technological ability patent

shares represented as compared to TFP and other estimates we correlated the change in

patent shares with measures for Efficiency Index of countries reported in Russell and

Kumar (2002) for comparable years. The correlation coefficient between the two measures

was about 0.30.

A major drawback of the USPTO dataset however is that the US accounts for a

large proportion of all patents granted, though its own share of patents has been decreasing

over time. Thus, the patent share of the US alone was over 90% in 1950 and fell over time,

but was still high at 55% in 1995. To get a clearer picture about the role of new countries

in patenting, we consider all foreign patents issued by the USPTO i.e. we exclude US

patents. Appendix 1 describes the main features of the data used in this study.

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We compute the Herfindahl index of concentration of patent shares across countries

as a summary measure of the uneven technological ability of nations at any point in time.

By definition

Ht= Sit2, (1)

where Sitis the share of the ith country in all (foreign) patents issued at time t.

We then exploit a particular decomposition of the Herfindahl index, which splits the

change in overall concentration into a turbulence effect and a regression effect.4

Ht= Ht-1+ Ht (2)

Substituting (1) into (2)

Ht= i(Sit)2+ 2 i(Sit-1Sit) (3)

In equation (3), the first term of the RHS measures patent share turbulence (the

concentration of the change in shares). Both positive and negative changes have the same

weight in this index and the larger the value of the turbulence the more changes there will

have been in patent shares. By construction the turbulence measure is always positive.

The second term is however, the more interesting one for tracking technological

catch-up by new countries. It measures the linear association between initial share and the

change in share, weighting large initial shares more than small ones. We call this the

Inverse Regression Effect, since negative values imply a regression of country shares

towards the mean.5 Negative values of the inverse regression effect come about due to

those that had initially larger patent shares being predominantly also those with negative

values of Sit, which occur when these countries lose patent shares. When small patentees

4For an application of this decomposition to study the evolution of market shares and concentration see

Kambhampati and Kattuman (2003).5This very similar to the Galtonian regressions used in Cantwell (1991a), in which the variance of

shares is analogously decomposed into a mobility effect (measured by one minus the correlationcoefficient), and a regression effect (measured by one minus the slope coefficient on lagged shares).

Since H = (V/2

+ 1) / N, where V and are respectively the variance and mean of the country sharesand N is the number of countries considered, while in our case the mean share it follows thatH = NV + (1/N). Thus, for a given N, H rises with V.

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have gained or lost patent shares these are given a smaller weight and the cross term will

have a smaller positive value than if the same were to happen to large patentees. As new

countries begin to make small gains in patent shares they erode the shares of existing

nations and tend to cause lower positive values for the turbulence term (turbulence tends to

be greatest when it is the largest countries that make significant gains and losses against

one another, since at that end changes in shares tend to be higher in absolute terms), and a

negative value for the inverse regression index. When some already dominant existing

countries are increasing their patent shares both terms will be positive and higher. We plot

the two terms over time in an exploratory graphical analysis. The results are discussed

Section 3.1 below.

2.2 Explaining technological catch-up

We follow the exploration of the dependent variable with a time series analysis

where the changes in the inverse regression index are explained by measures of

globalisation and human capital in the world economy. The data for these are drawn by

aggregating the data over countries from well-known data sources.

We use three measures of globalisation:

(i) Openness to trade as measured by the ratio of exports and imports to total

world income.

(ii) The share of international production in world income

(iii) The ratio of domestic to international (MNC-owned patents in host

countries) patents in the world economy

We also included two measures of human capital:

(iv) The share of tertiary educated population in the world economy

(v) The variance in the share of tertiary educated population in the world

economy

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In each case, we aggregated country data to obtain world averages. In order to

control for the effects of the internationalisation of the patent regime we introduced two

new variables the number of patents and the number of countries. The independent

variables used in the study, their data source and expected influence on technological

catch-up in the world economy is summarised in Table 1 below.

[Table 1 here]

Lastly, to assess causality between the IRI and each of the independent variables we

used Granger causality tests. In this exercise we ask the data to predict observed values of

the dependent variable using past lagged values. If the coefficient on the lagged

explanatory variables is significantly different from zero we infer that the explanatory

variable causes movements in the dependent variable. Since the explanatory variables are

all I(1) while IRI is I(0) we use the explanatory variables in their first differences.

3. Empirical analysis

3.1: Assessing periods of technological catch-up

Figure 1 below shows the overall trend in the Herfindahl index of foreign patents

granted by the USPTO. After a long period between 1954-1975 when overall

concentration hovered around 15%, the index rose sharply in the period between 1975-

1992, reaching a value of 28 % in 1992 but it fell again to levels close to 22%. The number

of countries over the entire period rose from about 40 to 60, with the bulk of the increase in

numbers coming in the decade of the 1950s.6

[Figure 1 here]

Figure 2 below plots the three terms of equation (3). Since patent numbers vary

widely year-on-year they can cause individual patent shares to fluctuate widely. To smooth

6See Appendix 1 for the number of countries by year.

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the data for these variations we also plot a four period moving average for the two RHS

terms.

[Figure 2 here]

Figure 2 shows that overall there was relatively little turbulence in the cross-country

distribution, and so the changes in the Herfindahl index were mostly due to changes in the

Inverse Regression Index. This finding is consistent with the view (Cantwell 1991b,

Vertova 1999) that technological advantages of countries are strongly sector-specific and

takes a long time to change.

Through much of the 1950s and 1960s the inverse regression index values were

negative, reflecting a loss of patent shares to new patentees. The negative values were

somewhat larger in the 1950s than in the 1960s, when they hovered between 0 and 0.5%.

The period 1992- 2001 has also been one of catch-up, with smaller patentees gaining patent

share. Again this is consistent with observations of decreasing inequality of world incomes

across countries reported by many studies.

In the intervening period (1972-92) the index turned positive and continued to rise

in value up until the mid-1980s. Thus, for much of the period since the mid-1970s a small

number of countries consolidated their technological positions and accounted for a growing

share of world technology generating capacity. This view of the overall concentration in

technological activity in a few countries from 1975-92 is consistent with the results of a

recent study by Kumar and Russell (2002). Using data envelopment analysis on cross-

country data, that study decomposed labour productivity growth into three components:

technological change (movements of the supposed world frontier), technological catch-up

(movements towards the world frontier) and capital deepening (movement along the

frontier). They found that while technological change contributed positively to growth in

the period 1965-90, the pattern was very dissimilar to overall productivity growth in that

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there were striking examples of technological regress for low-income countries. Further

they found larger than average contributions to growth for most high-income economies

suggesting technological change benefited the richer countries far more than poorer

countries.

3.2 Explaining movements in the inverse regression index

The order of integration of all the variables is reported in Table 2. The tests

indicate non-stationarity in all the main explanatory variables, but indicated stationarity in

the IRI. We thus used specifications based on first differences of the explanatory variables

explaining the level of IRI.

Table 3 reports some descriptive statistics of the variables we constructed. It is

worth noting the smaller number of observations for inward foreign investment and for

human capital. Table 4 reports the correlation matrix. The globalisation variables are quite

highly correlated with human capital and openness is highly correlated with human capital

and FDI variables.

Table 5 reports the results of the time series estimations. The first four columns

report the influence of the variables by themselves. We find that neither openness, nor

human capital, nor the ratio of domestic to foreign patenting by itself has an effect on

technological catch-up. However, the proportion of international production is by itself

negatively associated with technological catch-up.7

When we control for the extent of human capital, the influence of both trade and

proportion of inward FDI have negative signs and are significant. (The same results hold

when we use the variance of human capital in the world economy rather than its proportion,

though we do not report those results in Table 5). When we consider the influence of

7All equations display autocorrelation suggesting the need for additional lagged variables in the

specification. We take this into account when setting up the granger causality tests

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openness, inward FDI and of human capital, we find that proportion of human capital is not

a significant variable in its own right.

However, if we looked at the variance in human capital we find that it is positively

associated with movements of IRI, when openness and inward FDI are controlled for.

Including the ratio of domestic to foreign patents in the estimation renders only openness

significant. Including inward FDI renders openness insignificant.

These results accord with what is observed in empirical case studies. Though

studies of the Four Dragons and Japan show the role of openness and foreign firms in

technology acquisition and the technological capability building process, the role of human

capital is less clear. Narula and Wakelin (199?) also find that human capital affects export

performance only for the group of very developed countries. Furthermore, our definition

human capital is a more general measure than human capital acquired through training,

which is firm specific and may be expected to raise the productivity of capital employed

within the firm. On the other hand, case studies of science-based industries such as those

contained in Bresnahan and Gambardella (2004) have shown that areas of relative

concentration of human capital attract domestic and foreign science-based firms. Such

firms also tend to be more global in their selling operations.

3.3. Assessing Causality

We performed Granger causality tests to infer the direction of causality between the

variables. Since the first four columns of table 4 showed autocorrelation we included three

lags of the independent variable. The results of the Granger causality tests are reported in

Table 6. The results show that the two aspects of globalisation- international trade and

foreign investment- have a different causal relationship to technological catch-up

(measured by the IRI). Openness to trade granger causeschanges in the IRI. From the

regression equation in Table 5 we know that this relationship is negative and so increased

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trade causes technological catch-up. Neither of the human capital variables appear to cause

catch-up.

However, changes in the IRI granger cause movements in both IFDI and

DOMINT. From Table 5 we know that this relationship is negative, and so we can

conclude that downward movements in IRI (technological catch-up) induce increases in

IFDI. Similarly increases in IRI cause decreases in the DOMINT ratio or conversely

increases in international patenting by MNCs. These findings are consistent with the

observation made by many scholars that inward foreign investment seeks global sources of

competitive advantage and will be drawn to regions of advantage. It is also consistent with

the observation from studies at the firm level (Vuegelars and Cassiman 1998), which have

found that the evidence for foreign firms transferring technology is weak when their

(better) access to technology is controlled for.

4. Summary and conclusions

In this paper we use a patent based measure of technological catch-up in the world

economy to try and assess periods of catch-up as well as assess the factors that seem to

cause changes in catch-up.

We find a mild increase in the concentration of innovation across countries in the

period from 1970-90, and the existence of technological catch-up in the 1950s and 1960s

and again in the period 1992-2001.

As with many empirical studies we find the degree of openness and inward foreign

investment are associated with catch-up. However, causality tests reveal that only

openness to international trade causes technological catch-up. Growth in inward FDI and a

decrease in domestic patenting are caused by technological catch-up.

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The proportion of human capital in the world economy has no discernible effect on

technological catch-up, but increases in the concentration of human capital in the world

economy are associated with increases in the concentration of technology production.

However, the significance of both these human capital variables vanishes when we control

for the effect of international patenting by multinationals.

References:

Archibugi D. (1992): Patenting as an indicator of technological innovation: a

review, Science and Public Policy, Vol. 19, pp. 357-68.

Athreye, S. and R. Simonetti (2004): Technology, Investment and Growth in W.

Brown, S. Bromley and S. Athreye (eds): Ordering the International: History, Change and

Transformation, Pluto Press: London, August 2004.

Bell, M and K Pavitt (1997): Technological accumulation and industrial growth:

contrasts between developed and developing countries, in Archibugi, D and J. Mitchie

(eds) Technology, Globalisation and Economic Performance, Cambridge: Cambridge

University Press.

Bernard and Jones (1996): Technology and Convergence, Economic Journal

Vol.106 (6), pp.1037-1043.

Cantwell, J.A. (1991a): The international agglomeration of R&D, in Casson, M.C.

(ed.) Global Research Strategy and International Competitiveness, Oxford: Basil

Blackwell.

Cantwell, J.A. (1991b): Historical trends in international patterns of technological

innovation, in Foreman-Peck, J. (ed.), New Perspectives on the Late Victorian Economy:

Essays in Quantitative Economic History, 1860-1914, Cambridge and New York:

Cambridge University Press.

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Freeman, C (1997): The National system of innovation, in Archibugi, D and J.

Mitchie (eds) Technology, Globalisation and Economic Performance, Cambridge:

Cambridge University Press.

Griliches, Z. (1990): Patent statistics as economic indicators: a survey, Journal of

Economic Literature, Vol. 28, pp. 1661-707.

Kumar, S. and Russell, R.R. (2002): Technological change, technological catch-up

and capital deepening: relative contributions to growth and convergence, American

Economic Review, Vol

Lundvall, B- A. (1992): National Systems of Innovation. London: Pinter.

Nelson, R.R. and Rosenberg, N. (1993): Technical innovation and national systems,

in Nelson, R.R. (ed.),National Innovation Systems: A Comparative Analysis, Oxford and

New York: Oxford University Press.

Pavitt K.L.R. (1985): Patents statistics as indicators of innovative activities:

possibilities and problems, Scientometrics, Vol. 7, pp. 77-99.

Pavitt K.L.R. (1988): Uses and abuses of patent statistics, in van Raan, A.F.J. (ed.),

Handbook of Quantitative Studies of Science and Technology, Amsterdam: Elsevier

Science Publishers.

Quah, D. (1996): Twin peaks: Growth and convergence in models of distribution

dynamics,Economic JournalVol.106 (6), pp.1045-1055.

Schmookler J. (1950): The interpretation of patent statistics, Journal of the Patent

Office Society, Vol. 32, pp. 123-146.

Schmookler J. (1966): Inventions and Economic Growth, Cambridge, Mass.:

Harvard University Press.

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18

Vertova, G. (1999): Stability in national patterns of technological specialisation:

some historical evidence from patent data,Economics of Innovation and New Technology,

Vol. 8, pp. 331-354.


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TABLES

Table 1: Explanatory Variables used in the econometric analysis

Variable Description Data Source Span Expected

sign

OPEN(Import + Export) / real

GDP (1996 constant)

Unit: %

Penn Data

http://pwt.econ.upenn.edu/

1950-

2000

-

IFDI Inward FDI Stock / GDP,

Unit: %

World Investment Report

2004 (UNCTAD)

1980-

2000

-

DOMINT Ratio of domestic firm

patents to MNC patents

in host countries

USPTO data 1950-95 +

HUMCAP Tertiary enrolment /Population

Unit: %

World DevelopmentIndicators 2002

(World Bank)

1970-2000

-

HCAPCV Coefficient of variation

of human capital

Unit: %

World Development

Indicators 2002 CD-Rom

(World Bank)

1980-

2000

+

Controlvariables

PATENTS Total number of patents

in USPTO

USPTO data 1950-

2001

NUMBER

Total number ofcountries patenting USPTO data 1950-2001

Table 2: Unit Root Tests

Variable Order of Integration Data Span

HERF I(1) 1950-2001

PATENTS I(1) 1950-2001

OPEN I(1) 1950-2000

HUMCAP I(1) 1970-2000

DOMINT I(1) 1950-2001

HUMCAPCV I(1) 1970-2000

NUMBER I(1) 1950-2001

IFDI I(2) 1980-2000

Note:All tests are significant at the 5% level of significance


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Table

3:Descriptivestatistics

HERF

MOB2

OPEN

IFDI

DOM

INT

HUMCAP

HUMCAPCV

Mean

0.19

0.00

0.29

9.96

23.29

13.04

1.13

Median

0.16

0.00

0.28

9.09

17.31

13.02

0.93

Maximum

0.28

0.02

0.52

19.31

61.08

19.29

1.77

Mini

mum

0.14

-0.02

0.16

6.60

6.80

7.81

0.85

Std.Dev.

0.05

0.01

0.09

3.24

16.33

3.72

0.33

Skew

ness

0.68

-0.26

0.77

1.65

0.70

-0.03

1.12

Obse

rvations

52

52

51

21

52

28

29

Table

4:Crosscorrelationmatrix

DOMINT

IFDI

OPENNESS

HUMANCAP

HUMANCAPCV

D

OMINT

1.00

-0.74

-0.63

-0.74

0.53

IFDI

-0.74

1.00

0.93

0.94

-0.76

OP

ENNESS

-0.63

0.93

1.00

0.98

-0.88

HU

MANCAP

-0.74

0.94

0.98

1.00

-0.86

HUM

ANCAPCV

0.53

-0.76

-0.88

-0.86

1.00


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Table

5:Determinantsofchangein

theinverseregressionindex

(1)

(2

)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

CONST

ANT

0.001

0.006*

0.004

-0.00

0.007*

0.007**

0.0

10***

0.011***

0.006***

OPENN

ESS

-0.122

-0.412**

-0.453**

-0.437**

-0.371**

IFDI

-0.00

8***

-0.007*

-0.007*

-0.008***

DOMIN

T

-0.00

-0.001

HUMANCAPITAL

-0.001

0.008

-0.004

0

.000

HCAPC

V

0.219*

0.009

NUMBER

0.000

0.0

00

0.000

-0.00

0.000

0.000

0

.000

0.001

0.000

PATEN

TS

0.000

0.0

00

0.000

0.00

0.000

0.000

0

.000

0.000

0.000

Autocorrelation

Yes

N

o

Yes

Yes

No

No

No

No

No

R-squared

0.065

0.4

49

0.061

0.061

0.366

0.310

0

.568

0.672

0.357

observ

ations

50

20

27

51

27

17

17

18

28


22/26

Table 5: Tests of Granger causality for pairs of variables (lags included=3)

Variable pair Null Hypothesis Obs F-statistic Probability

(IRI, OPEN) OPEN does not

Granger Cause IRI

47 2.926 0.045

IRI does not Granger

Cause OPEN

0.364 0.779

(IRI, FDI) IFDI does not Granger

Cause IRI

17 2.629 0.108


Cause IFDI

4.040 0.040

(IRI, DOMINT) (DOMINT) does not

Granger Cause IRI

48 0.415 0.74


Cause (DOMINT)

2.640 0.062

(IRI, HUMCAP) HUMCAP does notGranger Cause IRI

24 0.658 0.589


Cause HUMCAP

0.352 0.788

(IRI, HUMCAPCV) HUMCAPCV does not

Granger Cause IRI

25 0.863 0.47


Cause HUMCAPCV

0.265 0.850


23/26

Appendix1:Descriptionofthedataset

TheUSPTOdatasetwehave

usedhereissimilarthatusedinCantwell(1991a).Itconsists

ofthenon-USpatentsissuedb

yyearand

countr

y.Thetotalnumberofpatents

ineachyearandthenumberofcountriesislistedinTableA1

below.

Table

A1.

USPTOdatausedinthe

analysis

Year

Patents

Number

Year

Patents

Number

Year

Patents

Number

Year

Patents

Number

1950

42952

41

1966

726

99

52

1982

57888

52

1998

1475

18

58

1951

44326

42

1967

650

56

52

1983

56861

54

1999

1534

85

58

1952

43614

47

1968

554

05

51

1984

67197

57

2000

1574

95

58

1953

40468

44

1969

675

57

51

1985

71659

55

2001

1660

37

57

1954

33808

41

1970

644

29

54

1986

70858

57

1955

30431

39

1971

783

15

55

1987

82949

59

1956

46810

47

1972

748

08

55

1988

77922

56

1957

42743

44

1973

741

43

54

1989

95505

59

1958

48330

45

1974

762

76

56

1990

90278

57

1959

52407

46

1975

720

00

55

1991

96513

57


24/26

1960

47170

43

1976

702

26

54

1992

97441

54

1961

48368

50

1977

652

69

55

1993

98342

56

1962

58950

50

1978

661

01

53

1994

101675

55

1963

42407

52

1979

488

54

55

1995

101419

56

1964

47381

50

1980

618

18

56

1996

109645

56

1965

62857

53

1981

657

71

55

1997

111983

57


25/26


26/26

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