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R&D around the world: the roles of firm- and country-specific

determinants

Abstract: To what extent can firm- and country-specific factors explain firm-level R&D?

Using a comprehensive dataset, which consists of 19,636 unique firms covering 49 countries

and spanning 1981-2013, we examine this question by capturing multiple dimensions of the

market and institutional characteristics. Our findings suggest that the firm-level determinants

have higher explanatory power than the country-level determinants. While firm-level

determinants explain 43% of the variations in the whole sample, country-level determinants

explain only 10%. In addition, we show that the effect is similar for countries with weaker

and stronger institutional scores related to R&D and for industries that are closer to R&D.

These results are robust after adopting alternative empirical methodologies, such as

hierarchical linear modelling and different measures of R&D intensity. Our study has strong

policy implications that show international firms when investing in developing countries,

need to focus tightly on their own organisation and capital structures based on how the

project is managed internally.

JEL Classifications: O30; O50; F01

Keywords: R&D; Institutional quality; Productivity; Globalisation.

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1. Introduction

Numerous studies in the past provide overwhelming evidence that firms engage in

R&D activity to gain sustained competitive advantages and market power (Hart, 1995;

Barney, 2000; Rothaermel, 2013). There is also substantial evidence that higher research

activity within a firm has a positive effect on a firm’s productivity (Griliches et al., 1991;

Griliches, 1995), as well as on its market valuation (Hall, 2000).* More recently, some studies

show strong links between R&D and institutions and their joint effect on the economic

growth of countries. For instance, Coe et al. (2009) argue that countries with strong patent

protection or legal system and where the local conditions permit ease of doing business,

benefit more from domestic R&D and international R&D spillovers. Similarly, we find that

high-income countries contribute most towards world technological progress. In 1995, the

largest seven industrialised (G7) countries in the world captured 84% of the world’s R&D

expenditure (Keller, 2004). Recent figures show that the high-income countries still generate

the bulk of global R&D expenditures, which was 79.7% in 2007 and a slightly lower share of

69% in 2013 (UNESCO, 2015)†. Thus, the extant literature provides sufficient evidence of

the importance of R&D and its relationship with the institutional elements of those countries.

However, does this imply that country-level factors, such as income, trade or growth

rates, matter most when it comes to R&D investment by firms? Recent evidence shows that

China’s share of research expenditure has almost doubled from 10.2% in 2007 to 19.6% in

2013. Moreover, 84% of this research expenditure in China is experimental as compared to

only 64.2% in the US (UNESCO, 2015). Another developing country, South Korea is one of

the top five countries in the world in research expenditure in 2013 ($69 billion) Thus, it

appears that the income status of a country may not be the sole driver of R&D intensity as

* See more recent works done by Toivanen et al. (2002), Del Monte and Papagni (2003), Hall et al. (2005); Bracker and Ramaya (2011); Sandner and Block (2011); García-Manjón and Romero-Merino (2012). † UNESCO Link: http://en.unesco.org/node/252279

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http://en.unesco.org/node/252279

recently believed in many developing and transitional economies. Further, to date, we find

very little or no evidence that uses proper empirical techniques to check the relative

importance of firm-level vis-à-vis country-level determinants of R&D.

R&D expenditure is often considered a risky investment by firms (Bhagat and Welch,

1995; Kothari et al., 2002; Coles et al., 2006). There is no certainty that investment in R&D

will always offer a high return and offset the costs associated with these investments.

Therefore, firms need to be mindful of the risks associated with the local business

environment and the institutional characteristics of the host country. The importance of

government policy or geographical location in R&D is highlighted by Porter (1990, 1994,

1998). Porter (1998) argues that the productivity and competitiveness of domestic and foreign

firms are strongly influenced by the quality of institutions in the country where these firms

are domiciled. For instance, if transaction costs are too high, there is a high level of red tape,

low quality of transport and research infrastructure, low protection of intellectual property

rights and other similar attributes. Thus, these firms are unable to compete on high service

strategies and end up providing low customer satisfaction. Other studies that show the

importance of institutional quality or intellectual property rights in determining the level of

competition and R&D are Guellec and Potterie (2004), Acemoglu and Akcigit (2012) and

Spulber (2013). While Guellec and Potterie (2004) show that institutional quality plays a key

role in determining the effect of R&D on the productivity growth of countries, Acemoglu and

Akcigit (2012) argue that policies on intellectual property rights (IPR) in a country should

provide greater protection to the technology leaders (innovative firms) to achieve higher

growth. Moreover, Spulber (2013) finds that antitrust policies and IPR policies are

complementary in providing incentives among firms to innovate. Thus, country-level

institutional characteristics play an important role in firm’s R&D investment decisions.

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Alternatively, with higher level of international capital flows among countries in the

form of international trade and foreign direct investment (FDI), the role of country-level

characteristics may diminish over time and the outcome of a R&D investment is then

dominated by firm-specific factors. For instance, internationally product diversified firms

may benefit more from R&D intensity than non-diversified firms in the same industry group

(Hitt et al, 1997). This is mainly because by integrating across country borders and

standardising their products, these internationally diversified firms minimise their opportunity

cost of scarce resources, such as R&D, and enjoy both economies of scale and scope

(Kochhar and Hitt, 1995). Moreover, technological opportunity and appropriability at the

firm-level differ greatly across industries and time (Gilbert, 2006). For instance, Basant and

Frikkert (1995) show that for Indian manufacturing firms, the returns to foreign technology

and in-house R&D varies greatly between the non-scientific firms and the scientific firms in

the early 1990s before the trade reforms took place in India. While the return to in-house

R&D was higher in the first group (64% in comparison to 1%), the return to foreign

technology was higher in the latter group (95% in comparison to 116%).

Then, to what extent do firm- and country-level determinants drive firm-level R&D?

The answer is still unclear from the extant literature. This study uses a comprehensive dataset

covering 123,019 firm-year observations from 49 countries spanning 1981-2013, and

examines this issue by capturing multiple dimensions of the market and institutional

characteristics. Our findings suggest that the firm-level determinants have higher explanatory

power than the country-level determinants. While firm-level determinants explain 43% of the

variations in the whole sample, country-level determinants explain only 10%. When we

include both firm- and country-level determinants in a single regression, they jointly explain

43%, which is almost the same as the variation explained by the firm-level determinants.

After bifurcating our sample based on weaker and stronger institutional scores related to

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R&D, such as political, financial and economic stability, protection of intellectual property

rights and others, firm-level determinants still dominate. However, under each category,

country-level determinants explain higher variations in the empirical models for those

countries with weaker institutional scores. The effect is also similar for different industry-

split, however, we find that the firm-level determinants have a higher explanatory power for

healthcare (41.9%), telecommunications (33.9%), industrial (26.7%) and technology (26.5%)

industries. These results are robust after adopting alternative empirical methodologies, such

as hierarchical linear modelling and different measures of R&D intensity.

The study contributes to the literature in two significant ways: first, to our knowledge,

this is the first study in the R&D literature to empirically test the relative importance of

country- versus firm-level determinants and captures both cross-sectional and time series

variations in the sample. Although this issue has been considered in the past in the context of

firm-level corporate decision outcomes, such as corporate governance undertaking, capital

structure, and leverage, similar approaches are yet to be undertaken in the context of R&D.‡

This is a relatively new area of research in the field of economics and finance and thus

remained under-researched until now. However, the topic is becoming increasingly popular

and requires more attention in the era of financial globalisation, which allows free movement

of capital flows among countries. For instance, when a multinational firm is deciding whether

to invest in a developing country, it is increasingly important to understand the dynamics of

the existing market factors. If findings suggest that the country-level determinants have a

very high explanatory power, then depending on the choice of the country, firms need to

‡ Doidge et al. (2007), Hugill and Siegel (2014) apply similar methods to test firm-versus-country level effects in the context of corporate governance. While Doidge et al. (2007) finds more support in favour of country-level determinants, Hugill and Siegel (2014) argue that firm-level determinants dominate country-level effects. Similarly, Jong et al. (2008) and Jõeveer (2013) test the relative significance of firm-specific and country-specific determinants in the leverage choice of firms and find that country-specific institutional determinants have a direct and stronger impact on leverage. In contrast, Gungoraydinoglu and Oztekin (2011) find that firm-level determinants of leverage explain two-thirds of the variations and country-level determinants only explain the remaining one-third. Giao (2010) applies similar techniques in the context of earnings quality of firms and finds that firm and industry determinants have more explanatory powers than country-level determinants.

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factor in all costs associated with the provision of infrastructure, protection of property rights

and other transactions costs, which would have been otherwise provided by the host country.

Here, to minimise costs the firm could be very selective about countries and make sure that

the country reflects a stable economy with strong economic growth and institutional

characteristics. Alternatively, if results suggest that country-level determinants have very low

explanatory power and most of the variations in the determinants of R&D are driven by firm-

level characteristics, then the choice of a country is probably less important for the firm to

make any new investment in R&D. In this case, to protect itself from the local business

environment it is essential for the firm to have a robust internal organisational structure.

Thus, by implementing tight governance regulations or having low leverage, high asset size

and improving upon similar firm-specific factors, the firm can ensure that investment in R&D

turns out to be value-enhancing.

Second, recent evidence suggests that there exists a geographical bias in the research

fields, such as in economics and finance, where an increasing number of empirical works are

published on topics related to the US market in contrast to a broad range of international

markets (Das et al., 2013; Karolyi, 2016). However, since the 1990s the impact of research

papers written in the areas of international economics and finance are higher and they also

received a higher number of citations (Card and DellaVigna, 2013; Karolyi, 2016). Karolyi

(2016) argues that an important factor limiting research works in a multi-country setting (in

addition to countries such as the US, China and some European countries) is the availability

of a comprehensive dataset, which are both expensive and only recently available to many

universities across the world.§ Our study clearly overcomes this limitation and considers this

unique database, consisting of firm-level data from 49 countries, covering both surviving and

ceased firms. To the best of our knowledge we are not aware of any earlier study that has

§ For example, Worldscope and DataStream were only launched in the early 1990s. Over 100 universities around the world just recently subscribed to these databases in 2015 (Karolyi, 2016).

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constructed such a comprehensive dataset for an empirical analysis. Yet another important

contribution of this study is the focus on the effect of institutional differences and industry-

differences among countries on the relationship between firm-versus-country level

determinants of R&D. Thus, findings from this study will help us form a deeper

understanding about the relative importance of the firm-versus-country level determinants of

R&D intensity in a multi-country setting.

The rest of the paper is organised as follows: Section 2 discusses the different firm-

level and country-level determinants of R&D citing the relevant literature. Data and

methodology is presented in Section 3. In Section 4 we present the empirical results and their

implications. Finally, Section 5 summarises the research findings and conclusions.

2. Determinants of firm R&D

2.1. Firm-level determinants of R&D

Before we empirically test the relative importance of country-versus-firm level

determinants, it is important to study each firm-level determinant of R&D based on the

relevant theoretical and empirical literature. The relevant set of firm-level determinants will

lead to less chances of model misspecification or biased results due to omitted variables. We

refer to the extant literature and use the following firm-level determinants: log of total assets,

price to book value ratio (PTBV), cash holding by firms, export intensity, age of the firm,

leverage, spillovers from other firms in the same industry and industry competition.

(i) Log of total assets : Following Schumpeter (1942), many empirical studies

show that firm size can influence firm-level R&D. Schumpeter (1942) argues that bigger

firms have more incentives to innovate and engage in R&D. However, empirical testing of

the Schumpeterian view often produces inconclusive results.** Many studies find no evidence

that R&D intensity is increasing with firm size, however, there is some evidence that R&D

** See Gilbert (2006) for a concise review of the literature of earlier studies.

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intensity is lower for the largest firms in an industry (Scherer, 1965; Mansfield, 1968). Other

studies that find no evidence of a positive relationship are Griliches (1980), Acs and

Audretsch (1990), Audretsch and Acs (1991) and Graves and Langowiz (1993). There is also

a large body of literature that find a positive relationship between the two (Link, 1981;

Cohen, 1995, Cohen and Klepper, 1996, Legge, 2000). Others, such as Pavitt et al. (1987)

and Tsai and Wang (2005), find a U-shaped relationship where both small and large firms are

more productive in R&D than medium-sized firms. To summarise, the inconclusive results in

these studies are mainly due to different measures of R&D (e.g. input versus output

elasticities), types of R&D (e.g. process versus product innovations) and due to differences in

the data sources and time periods of the study. However, all these studies confirm that firm

size can potentially influence R&D activities in firms. Thus, we include firm size as a

potential determinant of R&D and following Stulz (1994), we measure firm size by natural

logarithm of total assets.

(ii) Price to book value ratio (PTBV) : R&D is more common in firms where

assets are highly valued and backed by strong performance of stocks. In other words, if a firm

is generating poor returns on its stocks and most of the investors are at loss, that firm will

have limited resources to shift towards intangible assets, such as R&D. Fama and French

(1992, 1995) show that firms with a lower book-to-market value ratio (a low stock price

relative to book value) consistently yield higher earnings. If the firm is innovative in nature,

these higher earnings are directed towards R&D investments. Deng et al. (1999) find that

R&D activity measured by patent attributes are positively associated with subsequent stock

returns and market-to-book value ratios. Similarly, using data from 1,200 companies Lev and

Sougiannis (1999) show that lower book-to-market value companies have higher R&D

capital and vice versa. Further, they find that a higher value of R&D capital partially offsets

any negative impact on the firm coming from higher value of book-to-market ratio. In this

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study, following Fama and French (1992) we consider PTBV ratio to measure the valuation

of investments in a firm as well as growth opportunities. Thus, a higher PTBV ratio implies

that the assets of the firm are generating a higher value than the accounting value of those

assets and the firm has a higher incentive to undertake R&D activities.

(iii) Cash holdings by firms : Cash holdings are critical for R&D intensive firms,

particularly for those facing financial frictions (Kim et al., 1998). Any shortfall of cash

during future investments is balanced by the existing cash holding these firms build over

time. The marginal value of cash holdings is even higher for those firms which are financially

constrained internally but have valuable investment opportunities in the future (Faulkender

and Wang, 2006). These firms, with higher growth opportunities, use their cash holdings to

fund their future projects and minimise the cost of R&D investments instead of accessing

costly external capital (Almeida et al., 2004). Many studies have found that firms with higher

cash holdings are more R&D intensive and firms facing financial frictions use cash holdings

to smooth their R&D investments (Mikkelson and Partch, 2003; Pinkowitch and Williamson,

2007; Denis and Sibilkov 2010; Brown and Peterson, 2011).

(iv) Export intensity : With globalisation the link between export intensity and

firm-level R&D is an important topic of interest. Firms with superior technology and

differentiated products not only dominate their local market, but also gain a competitive edge

in the international market (Grossman and Helpman 1995). Although the effect of R&D on

export performance is evident in the literature both theoretically and empirically††, the

opposite effect, showing the effect of export intensity on R&D, did not receive the same level

of attention from researchers. Here, the exporters get access to foreign technology by

competing in the international market and subsequently improve their products in their

domestic market. This is otherwise known as the learning-by-exporting effect or learning-by-

†† For theoretical evidence see Krugman (1979), Greenhalgh (1990) and Grossman and Helpman (1993). For empirical evidence see Wakelin (1998), Sterlacchini (2001), Bleaney and Wakelin (2002) and Barrios et al. (2003).

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competing effect (see Girma et al., 2008), which is also consistent with the recent endogenous

growth literature as proposed by Romer (1990), Young (1991) and Grossman and Helpman

(1993). Thus, higher export propensity will impact the R&D intensity of firms positively.

Empirical studies that find support in favour of this hypothesis are Aw et al. (2007), Saloman

and Shaver (2005) and Girma et al. (2008). While the first two studies use firm-level data

from Taiwan and Spain, respectively, the latter compares firms between Great Britain and the

Republic of Ireland. All three studies come to the same conclusion that a higher export

intensity of firms increases the innovative capacity of domestic firms. Following the above

arguments, we take export intensity as a determinant of firm-level R&D and is measured by

total exports divided by total sales at the firm-level.

(v) Age of the firm : Although the relationship between firm performance and firm

age largely depends on the quality of local institutions, young and new firms remain

vulnerable in their initial years and pose a greater risk of failure. Once they pass a certain

stage (liability of the adolescence stage), the failure rate drops dramatically and the firms gain

more market power (Stinchcombe, 1965; Thorhill and Amit, 2003). With more market power,

firms are financially stronger and have more capacity to invest in R&D. Thus, based on the

resource-based-view of competition theory, as firms get older they are expected to become

more R&D intensive. Conversely, another stream of research argues that young firms enter

the market with higher productivity and above average growth rates that last for a longer

period (Majumder, 1997; Huergo and Jaumandreu, 2004). Moreover, there is a voluminous

literature that shows higher productivity is positively associated with R&D intensity (see

Griliches, 1995). Since evidence suggests that young firms are more productive, it is expected

that younger firms are also more R&D intensive. To control for firm age in our empirical

estimates we include the logarithm of one plus firm age in years as a determinant of R&D.

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(vi) Leverage : Firms are expected to invest more in R&D when industry is

performing well and financial distress is low. Opler and Titman (1994) examine the

relationship between financial distress and firm performance in the period 1972-1991 and

find that during industry downturns, firms with more leverage lose market share and obtain

less profits as compared to their less-leveraged firms. The relationship is even stronger for

firms with significant R&D expenditures. Other studies that find a negative relationship

between leverage and R&D intensity for the US are Friend and Lang (1988), Hall (1992) and

Bhagat and Welch (1995). Lang et al. (1996) show that the negative relationship between

leverage and growth opportunities of firms is only evident for firms or industries with low

value of Tobin’s Q, but does not hold for firms or industries that are highly valued. Thus,

firms that have good investment opportunities, such as in R&D, but are not well recognised

by the market are at real risk due to high leverage. Hall (2002) further argues that although

leverage is more useful to reduce the internal cost of capital in firms, it is of limited use for

R&D intensive firms. Since knowledge stock is induced by human capital and specific to few

firms, it is expected that R&D intensive firms will exhibit lower leverage than non-R&D

intensive firms.

(vii) Industry spillovers : The endogenous growth literature suggests that the

positive effect of R&D expenditures at firm-level on an economy’s long run growth is

augmented by their industry-wide spillover effect (see Romer 1990; Grossman and Helpman

1990; Raut 1995). For example, first R&D knowledge is gained at individual firm-level due

to investment in R&D. However, due to the nature of nonrivalness and the excludability of

R&D, it gradually flows from one firm to another and becomes social knowledge. As a result,

the productivity of all firms within that industry (inter-industry) and as well as outside the

industry (intra-industry), is enhanced. Thus, incoming R&D spillovers from another firm is

an important determinant of firm-level R&D (Cassiman and Veugelers, 1999). This is also

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true for firms in less developed countries where they first import foreign technology from

developed countries and then spend resources and perform in-house R&D to improve and

adopt this technology to their local use. Once this new technology is adopted by one firm in

the industry, the social knowledge then spreads to other firms in the same industry and the

productivity of the whole industry is improved (Jaffe, 1986; Raut, 1995). Following this

argument, we use industry spillover as a determinant of firm R&D. However, in the literature,

unlike other variables, there is no easy or direct empirical measure to capture R&D spillovers.

Here we follow Aggarwal et al. (2010) but in a different context, to construct a variable to

capture industry-based R&D spillovers. First, we calculate the mean R&D intensity of all

firms except that firm in the same industry, country and year. Then this procedure is repeated

for every firm in each industry, country and year to construct the spillovers from R&D at the

industry level.

(viii) Competition : The literature linking competition, firm size and R&D is

longstanding (see Gilbert, 2006). It mainly follows either Schumpeter (1934), supporting the

view that less competition induces higher R&D, or Fellner (1951) and Arrow (1962), in

favour of more competition leading to higher R&D. However, more recent studies agree with

the view that market concentration and R&D intensity are both endogenous to each other and

are jointly determined in a market equilibrium system (Nickell, 1996; Sutton, 1996; Blundell

et al., 1999; Marín and Siotis, 2007). In a recent study, Desmet and Parente (2015) argue that

larger markets are easier for adoption of advanced technology and larger firms produce more

number of innovations since they can spread the fixed costs of R&D over more units of

output. Based on a Schumpeterian endogenous growth framework, Aghion et al. (2005) show

that an inverted U-shaped relationship exists between the two variables where competition

and R&D intensity goes hand in hand at the very low levels of competition, however, once

the market starts expanding, the relationship is reversed and competition negatively affects

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R&D intensity. Other empirical studies that support this inverted U-shaped relationship are

Tingvall and Poldahl (2006), Yeh et al. (2008) and Ugur et al. (2016). Here we measure

competition by sales Herfindahl-Hirschman Index (HHI).‡‡ It is calculated by squaring the

market share of each firm in the same industry, country, year and then adding the resulting

numbers.

2.2. Country-level determinants of R&D

As compared to the firm-level determinants, the country-level determinants of

R&D are discussed extensively in the extant literature. The variables considered here are: real

GDP growth rate, level of real GDP per capita, institutional quality, financial development,

financial liberalisation, trade openness and inflation rate.

(i) GDP growth rate and GDP per capita : It is evident from the long-run

endogenous growth literature as proposed by Romer (1990), Grossman and Helpman (1990)

and Aghion and Howitt (1991), that for determining successful R&D, it important to control

for a country’s GDP growth rate and level of GDP per capita (see Barro, 1991). We use the

World Bank database to estimate the country-level GDP growth rate and real GDP per capita.

(ii) Institutional quality : As mentioned in the previous section, theoretical and

empirical literature suggests institutional quality as an important determinant of R&D. With

weak institutions, high red tapes and low protection of intellectual property rights, the

incentive for firms to engage in R&D will be lower. Moreover, stronger institutional quality

in less-developed countries plays an important role in successful technological adoption from

other developed countries. For instance, when a less developed country adopts a foreign

technology invented elsewhere, a stronger protection of intellectual property rights in the

developing country attracts foreign affiliates of multinational firms and results in a faster rate

of technology transfer between these two economies (Gustafsson and Segerstrom, 2011).

‡‡ The HHI is used widely in the industrial organisation literature to measure competition among firms (see Tirole, 1988).

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Here we use the international country risk guide (ICRG) index as the measure of institutional

quality. This is a widely accepted measure of country-level risk, which is calculated as a

weighted average of political, financial, and economic risk in a country.§§ The measure

provides useful information on the economic and financial stability of a country that affects

performance of a firm based in that country.*** The highest overall rating (theoretically 100)

indicates the lowest risk, and the lowest rating (theoretically zero) indicates the highest risk.

(iii) Financial development and financial liberalisation : It is important to consider

financial development and financial liberalisation as potential determinants of firm R&D. Financial

sector serves as a key determining link between R&D and productivity growth of economies

(Badunenko and Romero‐Ávila, 2013). Evidence shows that financial deepening or development

facilitates the accumulation of new ideas and financial liberalisation has a positive effect on R&D

only if the financial system is sufficiently liberalised (Ang, 2010). Moreover, financial liberalisation

can negatively impact R&D, particularly in the developing countries, by weakening the incentive to

save in the economy and shifting the new talents from the R&D sector to the financial sector (Ang,

2011). In this study, following the standard literature, we measure financial development by private

credit as a percentage of nominal GDP and financial liberalisation by the Chinn-Ito index based on

Chinn and Ito (2006). The Chinn-Ito index is constructed based on the financial restrictions in each

country as reported in the IMF’s annual report of exchange arrangements and exchange restrictions.†††

(iv) Trade openness : Following the argument of export intensity at firm-level,

higher trade openness could also affect R&D intensity in firms. Higher trade openness is

associated with greater spillovers of international R&D and local producers can improve their

products based on the import of foreign technology. Thus, trade openness captures

productivity improvements across countries based on international R&D spillovers (Coe and §§ For example, among recent studies see Djankov et al. (2006), Gradstein (2007) and Fan et al. (2009) for using ICRG index to compare institutional quality across countries.*** The data on ICRG is extracted from the international country risk guide index calculated by the Political Risk Services (PRS) group. The data is available for more than 140 countries. For a detailed methodology of the construction of this series, please consult the following link: https://www.prsgroup.com/about-us/our-two-methodologies/icrg††† The index was initially constructed till 2005, but regularly updated ever since and currently available for the year 2014.

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https://www.prsgroup.com/about-us/our-two-methodologies/icrg

https://www.prsgroup.com/about-us/our-two-methodologies/icrg

Helpman, 1995; Keller, 1998; Madsen, 2007). Following the standard literature, we calculate

trade openness by summing up the value of exports and imports as a percentage of nominal

GDP. We retrieve these variables from the World Bank database.

(v) Inflation rate : Finally, we also consider a country’s inflation rate as a

determinant of R&D intensity. More recently, studies, such as Chu and Lai (2013), Chu and

Cozzi (2014) and Chu et al. (2015) show that there is a strong negative relationship between

inflation and R&D. They also show theoretically that the elasticity of substitution between

real money balance and R&D is less than unity, implying that R&D is a decreasing function

of growth rate of money supply. Moreover, with the help of a two-country Schumpeterian

growth model, Chu et al. (2015) show that both domestic and foreign inflation negatively

affects domestic R&D investment in firms. To capture this in our model, we take annual

percentage change of consumer price index (CPI), provided by World Bank, as a measure of

inflation rate. The next section provides an overview of the empirical methodology and

discusses the data.

3. Data and empirical methodology

3.1. Sample data

Our sample consists of 123,019 firm-year observations and 922 country-year

observations from 49 countries. 123,019 firm-year observations pertain to 19,636 unique

firms. Our sample is devoid of survivorship bias as firms are added and delisted over the

sample period. The sample includes high income, medium income and low income

economies, and covers a period of 1981-2013 providing a robust panel data.

[Insert Table 1 here]

The list of countries and a summary of the sample is provided in Table 1. It is evident

from Table 1 that the sample is not evenly distributed, with some countries dominating the

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sample with a high number of firm-year observations while other countries have a low

number of firm-year observations. For instance, the US-listed firms comprise around 39% of

the firm-year observations. To crosscheck for country-level bias in the sample, we repeat our

empirical analysis after removing those countries where the firm-year observations are below

10. The empirical results are qualitatively the same if we include or exclude them in the

sample. Similarly, we repeat the analysis on a sub-sample after excluding the US-listed firms

since they dominate the sample. As before, our results are qualitatively similar even after

excluding the US-listed firms. We exclude firms that originate from offshore financial

centres, such as Panama, Cayman Islands, Bermuda and others. However, we find that our

baseline results remain qualitatively similar without their exclusion.


The summary statistics are presented in Table 2. We measure R&D intensity as R&D

expenditure scaled by total assets. The mean and standard deviation of R&D asset ratio is

0.04 and 0.072, respectively. The mean value of R&D intensity is significantly higher than its

median value (50th percentile). This suggests that many firms spend less (or no spending) on

R&D. Other firm-level determinants of R&D, e.g. PTBV, cash holdings, export intensity,

industry spillovers and competition also show a similar trend where the mean values are

higher than their median values. In contrast, the median value of log of total assets is 12.583,

which is close to the mean value of 12.677. For firm age, the median value (2.565) is higher

than the mean value (2.413), which shows that many firms in our sample are young and new

in the industry. At the country-level, GDP growth rate varies between -1.91% and 7.92%

capturing a very wide range of countries in our sample. The mean GDP growth rate in our

sample is 2.85%, which is close to the 50th percentile value of 2.56%. Overall, we find that

the key macroeconomic variables used in Table 2 fall within a reasonable range of values.

16

Next in Table 3 we present a correlation table of R&D determinants. While Panel A

shows the correlation matrix for the firm-level determinants of R&D, Panel B shows the

same for the country-level determinants of R&D. In the top panel, as expected we find high

correlation between industry spillovers and R&D ratio (0.60). There is a moderate level of

correlation between cash holdings and R&D ratio and cash holdings and industry spillovers

(0.45). Taking all findings together, they suggest a strong relationship among these three

variables in a univariate framework. In Panel A, we also note that the multicollinearity issue

is unlikely to influence empirical results. In Panel B, as predicted in the previous section,

there is a strong positive relationship between GDP per capita and institutional quality (0.63),

GDP per capita and financial development (0.65), and GDP per capita and financial

liberalisation (0.92). Financial liberalisation is also positively correlated with ICRG (0.63)

(measure of institutional quality) and private credits (0.59) (measure of financial

development).‡‡‡ Finally, as expected, higher inflation is found to be negatively correlated

with higher GDP per capita (-0.48), better institutional quality (-0.49) and financial

development (0.50).


3.2. Empirical methodology

Following Doidge et al. (2007), we consider the following empirical models:

Model 1: including only firm-level variablesRD i=α +β ' x i+ year dummies+εi (1)

Model 2: including only country-level variablesRD i=α +δ ' c i+ year dummies+ε i (2)

Model 3: including firm- and country-level variables

‡‡‡ To avoid multicollinearity problems, we run robustness tests by excluding one control variable, which had high correlation with another control variable, however, the results remain consistent. These results are available on request.

17

RD i=α +β ' x i+δ' c i+ year dummies+εi (3)

Model 4: including only country-level dummiesRD i=α +country dummies+ year dummies+εi (4)Model 5: including firm-level variables and country-level dummiesRD i=α +β ' x i+country dummies+ year dummies+εi (5)

where RDi is the R&D intensity of firm i. xi is a set of firm-level determinants, whereas ci is a

set of country-level determinant. The coefficients β and δ measure the sensitivity of firm-and

country-level variables on R&D intensity respectively. We employ three different measures

of R&D intensity as the dependent variable: R&D expenditure scaled by total assets, R&D

expenditure scaled by sales and R&D expenditure scaled by capital (net property, plant, and

equipment). We employ unbalanced panel data with year fixed effects to control for

unobservable heterogeneity over time, which is related to both the determinants of R&D and

R&D intensity. The empirical results are presented in the next section.

4. Empirical results and discussion

4.1. Baseline results

We present the baseline results in Table 4. Models 1 to 5 are estimated using three

different measures of R&D intensity: R&D expenditure scaled by the total asset (Panel A),

R&D expenditure scaled by the total sales (Panel B) and R&D expenditure scaled by the net

capital stock (Panel C), respectively. Under each panel, five different models are estimated to

better understand the effects of firm-versus-country level determinants on R&D intensity.

While in model 1, we include only firm-specific determinants, in model 2 only country-

specific determinants are included. Model 1 and 2 do not include country fixed effects.

Therefore, it is expected that these two models will capture firm-level and country-level

variations separately in our sample. Next, in model 3 we introduce country fixed effects;

however, we drop all firm-level and country-level variables to isolate the effects of firm-

18

specific and country-specific determinants of R&D. In model 4, we include both firm-level

and country-level variables, but no country fixed effects. Lastly, in model 5 we include the

firm-level variables and country fixed effects but not country-level determinants of R&D.

The rationale behind excluding country-level variables in model 5 is that country-fixed effect

should automatically control for country-specific variations of R&D in the model. All five

models include year fixed effects.


The adjusted R-squared in each panel shows the explanatory power for each of the

specifications. In all three panels, we find that the joint explanatory power of firm-level and

country-level determinants (model 4) is maximum. The value is 43.7% in Panel A, 25.2% in

Panel B and 33.6% in Panel C, respectively. However, these values are very close to the

adjusted R-squared values we get in model 1 and model 5, where only firm-level variables

are included in the regressions (42.7% in model 1 and 43.2% in model 5 of Panel A, 24.7% in

model 1 and 24.8% in model 5 of Panel B, and 32.5% in model 1 and 32.9% in model 5 of

Panel C, respectively). Similarly, the value of adjusted R-squared is lowest when we include

only country-level variables in model 2. In addition, adjusted R-squared falls considerably in

model 3, when only country-fixed effects are run. The R-squared value drops from 43.7% in

model 4 to 8% and 10% in model 1 and model 3 of Panel A, respectively. Equally, in Panel

B, the value drops from 25.2% in model 4 to 2.4% and 3.1% in model 1 and model 3,

respectively. Finally, in Panel C, the value drops from 33.6% in model 4 to 5.7% and 7.2% in

model 1 and model 3, respectively. Interestingly, we note that by country fixed effects (model

4 of each panel) has a slightly higher explanatory variable that country-level variables (model

2 of each panel). This suggests that country fixed effects are a good proxy of a set of

macroeconomic variables. In summary, after comparing the adjusted R-squared values from

19

all three panels, we find that the firm-level determinants dominate country-level determinants

for alternative specifications in our empirical analysis.

4.2. Country-level splits

Countries with similar income growth prospects sometimes vary significantly in their

institutional quality and political structures. Consequently, their policies surrounding

investment decisions in R&D may also differ noticeably. Moreover, within the same group of

low and middle income countries, some countries are better off because they have better

infrastructure or availability of on-the-job training services, as compared to other developing

countries. For instance, among developing countries, both India and China have been

recognised recently as two of the fastest growing nations in the world in terms of their

income per capita growth. Both countries have gone through various reforms towards more

open economies that has a positive effect on their economic growth. However, when we look

at determinants, such as their institutional settings, level of corruption, political stability,

these two countries look very different to each other (Bardhan, 2012). Similarly, R&D

investment decisions in these two countries would require different assessments of their local

markets. Thus, instead of grouping the countries into developed, developing and low-income

countries in terms of their income per capita, we split our sample based on various

institutional determinants related to R&D investments. The sample is divided into two

groups: below median (low) and above median (high) scores based on the following country-

level splits as suggested by the Schwab and Sala-i-Martin (2013) report and the extant

literature on institutional quality: International Country Risk Guide (ICRG) index,

intellectual property protection, innovation capacity, overall infrastructure quality, research

and training services availability and global competitive index.

20

Except ICRG index, the scores of all other measures are based on Schwab and Sala-i-

Martín (2013). These measures are carefully selected and they offer an assessment score of

the capacity, availability and quality of R&D related infrastructures in an economy, measured

between 1 (not available or poor quality) and 7 (widely available or of very high quality). §§§

For instance, a high score on intellectual protection rights and overall infrastructure quality

(such as close to 7) indicates that firms in that country enjoy better protection on new

innovations and also receive better support on R&D related infrastructure. Our final

determinant, global competitiveness index, is based on twelve major pillars of economic

performance: institutions, infrastructure, macroeconomic environment, health and primary

education, higher education and training, goods market efficiency, labour market efficiency,

financial market development, technological readiness, market size, business sophistication

and innovation. A high score on global competitiveness index (such as close to 7) indicates

that firms in that country are much more competitive on a global scale based on all these

twelve major pillars of economic performance. The results are presented in Table 5.


Since some models in our baseline specification reported in Table 4 are qualitatively

similar, we do not explicitly include all five models in Table 5 due to paucity of space.****

Consequently, we consider three models to capture the importance of firm-versus-country

level determinants of R&D. In model 1, to isolate the firm-level effect, we include all firm-

level determinants of R&D. This is same as equation 1 and compares with model 1 of Table

4. In model 2, we isolate country-level effect by running only country fixed effect and

without including any firm-level variables. This corresponds to equation 3 and compares with

model 3 of Table 4. Finally, in model 3 we include all firm-level determinants of R&D as §§§ See the Schwab and Sala-i-Martín (2013) report for more details. **** However, we recheck our results with all five models of our baseline specification and the results are consistent. After including the country-level determinants, the regressions yield consistently low explanatory power. The results are not presented here to save space but are available from the authors on request.

21

well as country fixed effect to capture their joint effect on R&D intensity. This corresponds to

equation 5 and compares with model 5 of Table 4. To conserve space, henceforth we only

include R&D expenses scaled by total assets as a dependent variable. However, in unreported

results we find that the results remain qualitatively similar using either R&D expenses by

total sales or net property, plant, and equipment.

The results are generally consistent across all types of county-level splits. In both

groups (above or below median scores) of country-level splits, we find the results are very

close to our baseline results. The explanatory power of firm-level regressions in model 1 and

3 are close to each other and both values are considerably higher than the explanatory power

of the country fixed effects model in model 2. For example, the adjusted R-squared value in

the ‘high’ group of ICRG index is 41.0% and 41.6% in models 1 and 3, respectively.

However, the value reduces to 7.8% in model 2 when only country fixed effect is included.

Similarly, the adjusted R-squared values of overall infrastructure quality in the ‘low’ group

are 8.2% and 38.6% in models 1 and 3, respectively, and significantly higher than the country

fixed effects model (14.3%). This is also true for all other country-level determinants. When

we compare the results between ‘high’ and ‘low’ groups under each category, one important

difference emerges. In each type of country-level determinants, country fixed effects have

more variations in the ‘low’ group as compared to the ‘high’ group. For example, using the

ICRG index, the adjusted R-squared value in model 5 of low group (12.8%) is higher than the

value in model 2 of high group (7.8%). Similarly, the explanatory power of model 11 (low

group of intellectual property rights) is higher than model 8 (high group of intellectual

property rights) (13.7% in model 11 and 6.4% in model 8, respectively). This shows that

country-level support becomes more important for those countries where infrastructure

related to R&D or quality of institutions are weak. These findings are consistent with the

argument that countries with lower institutional quality need greater protection of intellectual

22

property rights or R&D infrastructure from their government than countries with superior

institutional structures (Doidge et al., 2007).

4.3. Industry-level splits

Next, in this section we split our sample based on the industry classifications

benchmark (ICB) provided by FTSE International.†††† Similar to the country-level splits in the

previous section, the industry-level splits will help us to determine any differences across

industries, in particular, for those industries largely owned and regulated by government (for

example, utilities). In developing countries, barriers to entry are high and rent-seeking

activities are more common between different political lobby groups and state-owned

companies (Clarke and Xu, 2004). This is likely to distort the decision by firms to invest in

R&D. Thus, it is reasonable to believe that if firms in an industry group are largely owned

and controlled by government, then country-level determinants would prove to be significant

as compared to private sector firms that largely rely on firm-specific determinants.

As per ICB classification, firms can be identified into 9 main industries: basic

materials, consumer goods, consumer services, health care, industrials, oil and gas,

technology, telecommunications and utilities. We follow the same empirical specifications as

the country-level splits in the previous section. Model 1 under each industry category refers

to effects from firm-level determinants and model 3 is the same after including country fixed

effects. Conversely, in model 2 only country fixed effect is run to isolate the country-specific

effects on R&D. The results are summarised in Table 6.


The results are again consistent with our baseline model. The adjusted R-squared

value is highest in model 3 across all industries and comparable with the values in model 1.

†††† http://www.icbenchmark.com/

23

The explanatory power of the regression is lowest in model 2, when only country fixed effect

is included. Thus, irrespective of the type of industries, the firm-level determinants of R&D

jointly have more explanatory power than the country-level determinants of R&D. It is also

important to note that there are significant variations in R-square values across the nine

industry-groups. In model 3, the adjusted R-squared value is highest for the industry group

‘healthcare’ (41.9%), followed by ‘telecommunications’ (33.9%), ‘industrials’ (26.7%) and

‘technology’ (26.5%). The findings show that in these groups of industries, firm-level

determinants are very important to drive R&D. Although model 2 generates low explanatory

power, industries, such as ‘technology’ (16.7%), ‘utilities’ (13.9%) and ‘health care’ (11%)

indicate greater country-level support as compared to other groups of industries. As

mentioned earlier, these findings are intuitive since industries, such as utilities and health

care, depend largely on government policies and regulations in the local market and thus need

more support at the country-level.

4.4. Additional robustness tests

Finally, we consider a number of robustness checks of our baseline results in Tables 7

and 8. In Table 7, we consider stock of R&D as the dependent variable instead of investment

of R&D. Griliches (1981) using a simple ‘definitional model’ argues that the current market

value of a firm (equity and debt) is a function of current value of the firm’s tangible capital

stock (plant, equipment, inventories and financial assets) and firm’s intangible capital stock

(stock of R&D). Further, using the same idea, Hall (1993) claims that the stock market

valuation of R&D capital in the US manufacturing market collapsed during 1973-1983,

which was not clearly noticeable when R&D investment is used to analyse the same industry.

Thus, the study argues that R&D stock is a better measure than R&D investment to examine

the effect of innovation on market valuation of firms. Since then a number influential studies

24

use stock of R&D to measure research intensity (R&D stock divided by total assets or sales

or net capital stock) to capture R&D spillovers and their effects on productivity growth across

firms (Raut, 1995; Basant and Fikkert 1996; Hall et al., 2005; Hu et al., 2005; Parisi et al.,

2006). We use the perpetual inventory method to construct the R&D stock variable under the

assumption of 15 per cent depreciation rate following Hall (1993) and Hall et al. (2005). The

results are presented in Table 7.


To remain consistent with our baseline model, three different measures of R&D

intensity is used as the dependent variable: R&D stock divided by total assets, R&D stock

divided by total sales and R&D stock divided by net capital stock. Following the same

empirical specification, models 1, 4 and 7 show the effects of firm-level determinants, models

2, 5 and 8 consider only country fixed effects model and models 3, 6, and 9 combine the

earlier two specifications and show the joint effects of firm-level determinants and country

fixed-effects. The results are overall consistent with our baseline results. For each measure of

R&D intensity, the adjusted R-squares in models 3 (35.9%), 6 (23.1%) and 9 (27.8%) are

consistently higher and closer to the values in models 1 (35.3%), 4 (23%) and 7 (27.4%),

respectively. Conversely, for each measure of R&D intensity the adjusted R-squares are

found to be lowest in models 2 (8.8%), 5 (3.1%) and 7 (6.2%) and substantially lower than

the other models when country fixed effects are run. Overall, our findings suggest that firm-

level determinants dominate the explanatory power of the empirical models when we

measure research intensity by R&D stock instead of R&D investment.


As an additional robustness check, we use hierarchical lineal modelling (HLM) to

simultaneously estimate the effect of firm-versus-country level determinants of R&D

25

intensity. The application of this model is very common in the empirical literature as an

alternative method of testing hierarchical data, particularly when firms are nested within

specific industries and further nested within countries’ borders.‡‡‡‡ The results are presented

in Table 8. In addition to the full sample, we use the country-level splits in Table 5 to check

whether our main findings change. Since our baseline empirical technique is significantly

different to HLM, the results could not be compared directly. However, consistent to our

original findings, we find that firm- and country-level determinants jointly explain higher

than country-specific determinants. For the full sample, with 123,109 observations, while

firm- and country-level determinants of R&D intensity explain 69.61%, country-level

determinants explain just 4.52%. Similar trends are observed for various country-level splits.

For example, for countries with high ICRG index (above median score), 75.62% of R&D

intensity is explained by firm- and country-level determinants jointly and just 5.06% by

country-level determinants. For countries with low ICRG index (below median score), a

similar trend exists, however, jointly firm- and country-specific determinants explain

somewhat lower (68.98%) than the group with high ICRG index (75.62%). This is also

consistent with other country-level splits, such as the intellectual property protection,

innovation capacity and overall infrastructure quality.

5. Conclusion

To the best of our knowledge, this paper for the first time tests the role of firm-level

determinants vis-à-vis country-level determinants of R&D in a multi-country setting. We pay

particular attention to the institutional and industrial differences across countries and also to

the different measures of R&D intensity while testing the relationship between firm-and

country-level determinants of R&D.

‡‡‡‡ See Short et al. (2007) and Hugill and Siegel (2014) for application of HLM to test country- and firm-level determinants of corporate governance.

26

Our findings strongly suggest that firm-level and country-level determinants of R&D

jointly have the highest explanatory power in all occurrences. However, firm-level

determinants of R&D, such as asset size, cash holdings, export intensity, leverage, industry

spillover and competition, play a greater role in explaining R&D intensity of firms. In all

occurrences, the explanatory power of the firm-level determinants of R&D is very close to

the joint significance of the firm- and country-level determinants. In contrast, the country-

level determinants themselves have very low explanatory power in our empirical estimates,

i.e., while firm-level determinants explain 43% of the variations in the whole sample,

country-level determinants explain only 10%. Thus, we show evidence that the importance of

country-level characteristics has weakened due to the increased level of financial

globalisation that took place over recent years. Although in our empirical estimates, inclusion

of year fixed effects control for time trends such as this, overall results suggest that even for

developing and transitional economies firm-level determinants of R&D have more

explanatory power. This is also consistent with the recent findings from UNESCO that

countries, such as China and South Korea, are increasingly becoming important on the global

stage when it comes to their share of world R&D expenditures. Our result is robust

irrespective of the industry-classification and country-level institutional differences.

However, for industries that have close ties with government policies, such as utilities,

healthcare, and technology, there is some evidence that firm-level and country-level

determinants play a complementary role. We also apply alternative techniques, such as HLM,

and these results still hold.

In conclusion, we find evidence that strongly suggests firm-level determinants of

R&D are more important than country-level determinants. Moreover, these findings show

that international firms when investing in developing countries, need to focus tightly on their

own organisation and capital structures based on how the project is managed internally. This

27

also has strong policy implications when considering foreign direct investments (FDI) by

firms. For successful turnover and high efficacy, the partner company in the host country

should focus on the presence of robust firm-level characteristics where R&D is valued highly.

As a result, capital will flow more freely among countries and global investment on R&D

will increase.

28

Appendix 1: Variable definitions

Variable Definition SourceA. Country-level variables

1 GDP Growth Rate Annual percentage growth rate of GDP at market prices based on constant local currency.

World Bank

2 GDP per capita Gross domestic product divided by midyear population

World Bank

3 Market Cap. (% of GDP)

Market capitalisation (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies.

World Bank

4 International Country Risk Guide (ICRG)

Measure of country-level risk, which is calculated as a weighted average of political, financial, and economic risk in a country.

Political Risk Services (PRS) group

5 Private Credit Measure of financial depth, which is calculated as the amount of total domestic private credit in a country as a percentage of nominal GDP.

World Bank

6 Trade Openness We calculate trade openness by summing up the value of exports and imports as a percentage of nominal GDP.

World Bank

7 Chinn-Ito-Index The Chinn-Ito index is constructed based on the financial restrictions in each country as reported in the IMF’s annual report of exchange arrangements and exchange restrictions. See Chinn and Ito (2006) for details.

http://web.pdx.edu/~ito/Chinn-Ito_website.htm

8 Inflation Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly.

World Bank

9 Intellectual Property Protection

In your country, how strong is the protection of intellectual property, including anti-counterfeiting measures? [1 = extremely weak; 7 = extremely strong]

World Economic Forum 2013 Report

10 Innovation Capacity In your country, to what extent do companies have the capacity to innovate? [1 = not at all; 7 = to a great extent]


11 Overall Infrastructure Quality

An assessment of the general infrastructure (transport, telephony, energy, etc.) in a country. The rating varies from 1 to 7; 1 representing extreme underdevelopment, and 7 suggesting an extensive and efficient infrastructure in a given country.


12 Research & Training Services Availability

In your country, to what extent are high-quality, specialised training services available? [1 = not available at all; 7 = widely available]


13 Global Competitiveness Index

This measure is based on twelve major pillars of economic performance: institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, goods market efficiency, labour market efficiency, financial market development, technological readiness, market size, business sophistication and innovation.


B. Firm-level variables 1 R&D Asset Ratio (using

investment method)Research & development expenses scaled by total assets

Author’s own calculation

29

2 R&D Sales Ratio (using investment method)

Research & development expenses scaled by total sales


3 R&D Capital Ratio (using investment method)

Research & development expenses scaled by net property plant and equipment


4 R&D Asset Ratio (using stock method)

We follow perpetual inventory method to construct the R&D Asset stock variable. Following Hall (1983) and Hall et al. (2005) we assume 15 per cent depreciation rate


5 R&D Sales Ratio (using stock method)

We follow perpetual inventory method to construct the R&D Sales stock variable. Following Hall (1983) and Hall et al. (2005) we assume 15 per cent depreciation rate


6 R&D Capital Ratio (using stock method)

We follow perpetual inventory method to construct the R&D Capital stock variable. Following Hall (1983) and Hall et al. (2005) we assume 15 per cent depreciation rate


7 Log Asset Log of total asset (reported in $, millions), deflated to 2004 price. Country-specific Consumer Price Index (CPI) data from World Bank is used to adjust the total asset to 2013 price.


8 PTBV Price to Book Value Worldscope

9 Cash Holding Cash and short term equivalents scaled by total asset

Worldscope

10 Export Intensity Export scaled by the total assets Worldscope11 Log (1+Firm Age) Log of one plus firm incorporation year Worldscope12 Leverage Total long-term debt scaled by total assets Worldscope13 Industry Spillover Average R&D intensity of other firms in the

same industry, country, and year.Author’s own calculation

14 Competition It is calculated by squaring the market sales share of each firm in the industry within the country and then adding the resulting numbers.


30

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36

Table 1: Sample description

No Country Firm-YearsFirm-Years

%Country-

YearsCountry-Years %

1 Australia 2,816 2.29% 29 3.15%2 Austria 202 0.16% 23 2.49%3 Belgium 253 0.21% 13 1.41%4 Brazil 104 0.08% 10 1.08%5 Canada 4,412 3.59% 33 3.58%6 Chile 72 0.06% 11 1.19%7 China 3,266 2.65% 19 2.06%8 Denmark 332 0.27% 26 2.82%9 Egypt 56 0.05% 9 0.98%10 Finland 792 0.64% 26 2.82%11 France 2,465 2.00% 30 3.25%12 Germany 3,235 2.63% 33 3.58%13 Greece 336 0.27% 18 1.95%14 Hong Kong 184 0.15% 19 2.06%15 India 8,568 6.96% 23 2.49%16 Indonesia 365 0.30% 19 2.06%17 Ireland 283 0.23% 28 3.04%18 Israel 733 0.60% 21 2.28%19 Italy 590 0.48% 28 3.04%20 Japan 20,601 16.75% 33 3.58%21 Jordan 14 0.01% 2 0.22%22 Kuwait 84 0.07% 9 0.98%23 Luxembourg 19 0.02% 4 0.43%24 Malaysia 1,155 0.94% 25 2.71%25 Mexico 83 0.07% 13 1.41%26 Netherlands 503 0.41% 22 2.39%27 New Zealand 173 0.14% 19 2.06%28 Norway 365 0.30% 21 2.28%29 Oman 43 0.03% 9 0.98%30 Pakistan 65 0.05% 11 1.19%31 Peru 14 0.01% 7 0.76%32 Philippines 137 0.11% 18 1.95%33 Poland 229 0.19% 9 0.98%34 Portugal 25 0.02% 2 0.22%35 Russia 192 0.16% 9 0.98%36 Saudi Arabia 115 0.09% 9 0.98%37 Singapore 951 0.77% 18 1.95%38 South Africa 435 0.35% 26 2.82%39 South Korea 2,752 2.24% 25 2.71%40 Spain 181 0.15% 11 1.19%41 Sri Lanka 30 0.02% 11 1.19%42 Sweden 1,041 0.85% 32 3.47%43 Switzerland 1,455 1.18% 27 2.93%44 Taiwan 4,821 3.92% 21 2.28%45 Thailand 548 0.45% 14 1.52%46 Turkey 1,086 0.88% 22 2.39%47 United Arab Emirates 43 0.03% 10 1.08%48 United Kingdom 8,725 7.09% 32 3.47%49 United States 48,070 39.08% 33 3.58% Total 123,019 922

In this table, we report sample description along with firm-year and country-year observations.

37

Table 2: Summary statistics DistributionVariable Mean StDev 5th 25th 50th 75th 95th

R&D Asset Ratio 0.040 0.072 0.000 0.000 0.012 0.046 0.175Log Asset 12.677 2.021 9.491 11.310 12.583 13.963 16.221PTBV 2.568 3.406 0.400 0.980 1.690 2.920 7.480Cash holding 0.178 0.187 0.006 0.042 0.115 0.244 0.591Export Intensity 0.260 0.299 0.000 0.000 0.146 0.454 0.898Log (1+Firm Age) 2.413 0.864 0.693 1.946 2.565 3.045 3.584Leverage 0.130 0.147 0.000 0.003 0.085 0.208 0.419Industry Spillover 0.044 0.054 0.000 0.007 0.022 0.064 0.152Competition 0.153 0.145 0.038 0.061 0.098 0.188 0.460GDP Growth 2.854 2.777 -1.911 1.613 2.668 4.092 7.923GDP per capita 32036.021 13421.270 1010.309 30111.098 35324.352 40938.063 45420.188Market Cap. (% of GDP)

95.689 56.214 39.352 61.122 92.550 125.110 152.965

ICRG 79.753 5.804 67.850 76.000 81.530 83.860 86.850Private Credit 1.404 0.506 0.471 1.071 1.461 1.839 2.016Trade Openness 0.438 0.430 0.175 0.225 0.300 0.533 1.006Chinn-Ito Index 1.830 1.209 -1.189 2.389 2.389 2.389 2.389Inflation 0.029 0.039 -0.004 0.015 0.024 0.034 0.089

In this table, we report summary statistics of key variables. R&D Asset Ratio is research & development expenses scaled by total assets. Log Asset is log of total asset (reported in $, millions), deflated to 2004 price. Country-specific Consumer Price Index (CPI) data from World Bank is used to adjust the total asset to 2013 price. PTBV is price to book value. Cash holding is cash and short term equivalents scaled by total asset. Export Intensity is export scaled by the total assets. Log (1+Firm Age) is natural logarithm of one plus firm incorporation year. Leverage is total long-term debt scaled by total assets. Industry Spillover is average R&D intensity of other firms in the same industry, country, and year. Competition is calculated by squaring the market sales share of each firm in the industry within the country and then adding the resulting numbers. GDP Growth Rate is annual percentage growth rate of GDP at market prices based on constant local currency. GDP per capita is gross domestic product divided by midyear population. Market Cap. (% of GDP) is market capitalisation (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. International Country Risk Guide (ICRG) is a measure of country-level risk, which is calculated as a weighted average of political, financial, and economic risk in a country. Private Credit is a measure of financial depth, which is calculated as the total amount of domestic private credit in a country as a percentage of nominal GDP . Trade Openness is calculated by summing up the value of exports and imports as a percentage of nominal GDP. Chinn-Ito-Index is constructed based on the financial restrictions in each country as reported in the IMF’s annual report of exchange arrangements and exchange restrictions. See Chinn and Ito (2006) for details. Inflation is measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly.

38

Table 3: Correlation coefficientPanel A: Firm-Level Variables

R&D Asset

Ratio Log Asset PTBV Cash holdingExport

IntensityLog (1+Firm

Age) LeverageIndustry Spillover

R&D Asset Ratio 1Log Asset -0.217*** 1PTBV 0.224*** -0.0669*** 1Cash holding 0.455*** -0.221*** 0.209*** 1Export Intensity 0.126*** 0.176*** 0.0209*** 0.0590*** 1Log (1+Firm Age) -0.149*** 0.315*** -0.140*** -0.234*** 0.0864*** 1Leverage -0.193*** 0.272*** -0.0143*** -0.349*** -0.0550*** 0.0565*** 1Industry Spillover 0.604*** -0.153*** 0.194*** 0.451*** 0.170*** -0.146*** -0.168*** 1Competition 0.0518*** 0.0191*** 0.0218*** 0.0130*** 0.252*** -0.0464*** -0.0207*** 0.0978***

Panel B: Country-Level Variables

GDP GrowthGDP per

capitaMarket Cap. (% of

GDP) ICRG Private CreditTrade

OpennessChinn-Ito

Index InflationGDP Growth 1GDP per capita -0.573*** 1Market Cap. (% of GDP) 0.0645*** 0.193*** 1ICRG -0.186*** 0.634*** 0.0748*** 1Private Credit -0.468*** 0.650*** 0.249*** 0.359*** 1Trade Openness 0.0966*** -0.0829*** 0.430*** 0.0283*** -0.235*** 1Chinn-Ito Index -0.575*** 0.923*** 0.162*** 0.630*** 0.587*** -0.133*** 1Inflation 0.298*** -0.479*** -0.109*** -0.487*** -0.496*** 0.0177*** -0.474*** 1

In this table, we report correlation coefficient between variables. In Panel A, we report correlation coefficient between firm-level variables, while in Panel B, we report correlation between country-level variables. R&D Asset Ratio is research & development expenses scaled by total assets. Log Asset is log of total asset (reported in $, millions), deflated to 2004 price. Country-specific Consumer Price Index (CPI) data from World Bank is used to adjust the total asset to 2013 price. PTBV is price to book value. Cash holding is cash and short term equivalents scaled by total asset. Export Intensity is export scaled by the total assets. Log (1+Firm Age) is natural logarithm of one plus firm incorporation year. Leverage is total long-term debt scaled by total assets. Industry Spillover is average R&D intensity of other firms in the same industry, country, and year. Competition is calculated by squaring the market sales share of each firm in the industry within the country and then adding the resulting numbers. GDP Growth Rate is annual percentage growth rate of GDP at market prices based on constant local currency. GDP per capita is gross domestic product divided by midyear population. Market Cap. (% of GDP) is market capitalisation (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. International Country Risk Guide (ICRG) is a measure of country-level risk, which is calculated as a weighted average of political, financial, and economic risk in a country. Private Credit is a measure of financial depth, which is calculated as the total amount of domestic private credit in a country as a percentage of nominal GDP. Trade Openness is calculated by summing up the value of exports and imports as a percentage of nominal GDP. Chinn-Ito-Index is constructed based on the financial restrictions in each country as reported in the IMF’s annual report of exchange arrangements and exchange restrictions. See Chinn and Ito (2006) for details. Inflation is measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly.

39

Table 4: Baseline resultsPanel A: Dependent Variable is R&D Expenses Scaled by Total Assets (1) (2) (3) (4) (5)Dep Variable R&D Expenses Scaled By Total AssetsLog Asset -0.00393*** -0.00458*** -0.00474***

(-2.89) (-2.81) (-3.51)PTBV 0.00179*** 0.00180*** 0.00178***

(8.31) (9.34) (8.76)Cash holding 0.0777*** 0.0732*** 0.0750***

(8.55) (6.95) (8.49)Export Intensity 0.0151*** 0.0183*** 0.0179***

(3.28) (3.60) (3.42)Log (1+Firm Age) 0.00137 0.000686 0.000729

(1.67) (0.84) (1.05)Leverage -0.00840*** -0.00896*** -0.00905***

(-2.88) (-2.98) (-2.73)Industry Spillover 0.627*** 0.617*** 0.607***

(29.23) (26.32) (26.08)Competition -0.00298 -0.000558 0.00594

(-0.60) (-0.12) (1.63)GDP Growth 0.00352*** -0.000309**

(4.31) (-2.14)Log (GDP per capita) 0.0165** -0.000208

(2.46) (-0.09)Market Cap. (% of GDP) 0.0000835 -0.0000105

(1.54) (-0.73)ICRG -0.00161** 0.0000761

(-2.04) (0.37)Private Credit -0.00882 0.00129

(-1.20) (0.48)Trade Openness -0.0139*** -0.00459**

(-3.90) (-2.12)Chinn-Ito Index 0.00853** 0.00199

(2.17) (0.94)Inflation -0.00846 0.0152

(-0.23) (1.38)Constant 0.0480*** -0.0351 -0.00338 0.0496 0.0392**

(2.70) (-0.72) (-1.02) (1.47) (2.59)N 123,019 107,159 123,019 107,159 123,019Adj. R2 42.7% 8.0% 10.1% 43.7% 43.2%Firm Variables? Yes No No Yes YesCountry Variables? No Yes No Yes NoCountry Fixed Effect? No No Yes No YesYear Fixed Effect? Yes Yes Yes Yes Yes

Panel B: Dependent Variable is R&D Expenses Scaled by Total Sales (1) (2) (3) (4) (5)Dep Variable R&D Expenses Scaled By Total SalesLog Asset -0.00809*** -0.00786*** -0.00758***

(-7.18) (-6.57) (-7.07)PTBV 0.00201* 0.00211* 0.00184*

(1.92) (1.95) (1.94)Cash holding 0.442*** 0.447*** 0.448***

(7.32) (7.04) (7.17)Export Intensity -0.0185* -0.0218 -0.0211*

(-1.71) (-1.65) (-1.77)Log (1+Firm Age) -0.00440 -0.00565** -0.00608**

(-1.61) (-2.34) (-2.29)Leverage 0.0810*** 0.0816*** 0.0784***

(4.75) (3.97) (3.77)Industry Spillover 0.514*** 0.526*** 0.512***

(10.79) (10.94) (9.53)Competition 0.0333 0.00752 0.0194

(1.44) (0.28) (0.61)GDP Growth 0.00861*** -0.00259*

(4.68) (-1.74)Log (GDP per capita) 0.0466*** -0.00297

(2.98) (-0.63)Market Cap. (% of GDP) 0.000235 0.0000131

(1.68) (0.37)ICRG -0.00403** 0.000811

(-2.06) (1.11)Private Credit -0.0212 -0.0176**

(-1.50) (-2.14)

41

Trade Openness -0.0302*** -0.00531(-4.35) (-0.72)

Chinn-Ito Index 0.00878 0.00274(0.96) (0.56)

Inflation -0.0265 0.0274(-0.27) (0.90)

Constant 0.0663*** -0.143 -0.0713*** 0.0437 0.0356**(5.39) (-1.35) (-9.32) (0.67) (2.44)

N 122,225 106,504 122,305 106,443 122,225Adj. R2 24.7% 2.4% 3.1% 25.2% 24.8%Firm Variables? Yes No No Yes YesCountry Variables? No Yes No Yes NoCountry Fixed Effect? No No Yes No YesYear Fixed Effect? Yes Yes Yes Yes Yes

Panel C: Dependent Variable is R&D Expenses Scaled by Net PPE (1) (2) (3) (4) (5)Dep Variable R&D Expenses Scaled By Net PPELog Asset -0.0767*** -0.0801*** -0.0804***

(-4.76) (-3.71) (-4.55)PTBV 0.0177*** 0.0168*** 0.0162***

(5.92) (6.37) (6.36)Cash holding 2.279*** 2.268*** 2.292***

(7.88) (7.22) (8.62)Export Intensity 0.0960 0.111 0.0789

(1.54) (1.56) (1.07)Log (1+Firm Age) 0.000767 -0.0130 -0.0123

(0.06) (-1.16) (-1.01)Leverage 0.0988 0.0716 0.0393

(1.54) (1.07) (0.54)Industry Spillover 0.545*** 0.510*** 0.517***

(22.81) (22.51) (26.18)Competition 0.0567 -0.100 -0.00167

(0.46) (-0.74) (-0.01)GDP Growth 0.0593*** -0.00834*

(4.87) (-1.85)Log (GDP per capita) 0.336*** 0.0451

(3.16) (1.12)Market Cap. (% of GDP) 0.00112 -0.0000262

(1.38) (-0.12)ICRG -0.0433*** -0.00630*

(-2.78) (-1.91)Private Credit -0.120 -0.0779*

(-1.43) (-1.76)Trade Openness -0.156** -0.0588

(-2.44) (-1.44)Chinn-Ito Index 0.114* 0.0326

(1.82) (0.92)Inflation -0.266 0.199

(-0.41) (0.85)Constant 0.764*** -0.0230 -0.796*** 0.970** 0.704***

(4.16) (-0.03) (-9.46) (2.11) (5.03)N 122,240 106,567 122,288 106,534 122,240Adj. R2 32.5% 5.7% 7.2% 33.6% 32.9%Firm Variables? Yes No No Yes YesCountry Variables? No Yes No Yes NoCountry Fixed Effect? No No Yes No YesYear Fixed Effect? Yes Yes Yes Yes Yes

In these tables, we present the baseline results. Models 1 to 5 are estimated using three different measures of R&D intensity: R&D expenditure scaled by the total asset (Panel A), R&D expenditure scaled by the total sales (Panel B) and R&D expenditure scaled by the net capital stock (Panel C), respectively. Under each panel, five different models are estimated. While in model 1, we include only firm-specific determinants, in model 2 only country-specific determinants are included. Model 1 and 2 do not include country fixed effects. In model 3 we introduce country fixed effects; however, we exclude all firm-level and country-level variables. In model 4, we include both firm-level and country-level variables, but no country fixed effects. Lastly, in model 5 we include the firm-level variables and country fixed effects but not country-level determinants of R&D. All five models include year fixed effects. The definitions of other variables are given in Appendix 1 of this paper. * statistically significant at the 10 percent, ** statistically significant at the 5 percent and *** statistically significant at the 1 percent level. t-stats are given in parenthesis and are based on robust standard errors.

42

Table 5: Country-level splits

Model Adj. R2Firm

Variables?

Country Fixed

Effect?

Year Fixed

Effect? NIC

RG

High(1) 41.0% Yes No Yes 72,806(2) 7.8% No Yes Yes 72,806(3) 41.6% Yes Yes Yes 72,806

Low(4) 45.0% Yes No Yes 47,955(5) 12.8% No Yes Yes 47,955(6) 45.4% Yes Yes Yes 47,955

Inte

llect

ual

prop

erty

pr

otec

tion High

(7) 37.9% Yes No Yes 60,698(8) 6.4% No Yes Yes 60,698(9) 38.6% Yes Yes Yes 60,698


Inno

vatio

n C

apac

ity High(13) 41.4% Yes No Yes 101,050(14) 7.1% No Yes Yes 101,050(15) 42.0% Yes Yes Yes 101,050


Ove

rall

Infr

astr

uctu

re

Qua

lity High

(19) 42.7% Yes No Yes 88,998(20) 7.3% No Yes Yes 88,998(21) 43.1% Yes Yes Yes 88,998


Res

earc

h &

T

rain

ing

Serv

ices

A

vaila

bilit

y High(25) 41.6% Yes No Yes 100,999(26) 6.6% No Yes Yes 100,999(27) 42.1% Yes Yes Yes 100,999


Glo

bal

Com

petit

ive

Inde

x

High(31) 41.6% Yes No Yes 102,266(32) 06.7% No Yes Yes 102,266(33) 42.1% Yes Yes Yes 102,266


In this table, we consider three models to capture the importance of firm-versus-country level determinants of R&D. In model 1, we include all firm-level determinants of R&D. This is same as equation 1 and compares with model 1 of Table 4. In model 2, we include only country fixed effect and without including any firm-level variables. This corresponds to equation 3 and compares with model 3 of Table 4. Finally, in model 3 we include all firm-level determinants of R&D as well as country fixed effect. This corresponds to equation 5 and compares with model 5 of Table 4. To conserve space, we only include R&D expenses scaled by total assets as a dependent variable. The definitions of other variables are given in Appendix 1 of this paper. We include control variables in the regression but do not report here due to paucity of space. * statistically significant at the 10 percent, ** statistically significant at the 5 percent and *** statistically significant at the 1 percent level. t-stats are given in parenthesis and are based on robust standard errors.

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Table 6: Industry-wise firm- and country-level determinants explanatory power (1) (2) (3)

Firm variables only Country fixed effects onlyFirm variables & country

fixed effectsBasic Materials

N 13,978 13,978 13,978Adj. R2 12.4% 9.0% 16.3%

Consumer GoodsN 20,256 20,256 20,256Adj. R2 16.1% 6.3% 17.5%

Consumer ServicesN 9,885 9,885 9,885Adj. R2 13.1% 2.8% 13.7%

Health CareN 12,619 12619 12,619Adj. R2 40.9% 11.0% 41.9%

IndustrialsN 38,576 38,576 38,576Adj. R2 25.5% 9.3% 26.7%

Oil & GasN 4,114 4,114 4,114Adj. R2 18.7% 2.1% 19.7%

TechnologyN 20,769 20,769 20,769Adj. R2 24.4% 16.7% 26.5%

TelecommunicationsN 740 740 740Adj. R2 32.8% 7.9% 33.9%

UtilitiesN 2,082 2,082 2,082Adj. R2 6.9% 13.9% 17.3%

In this table, we split our sample based on industry classifications benchmark (ICB) provided by FTSE International. As per ICB classification, firms can be identified into 9 main industries: basic materials, consumer goods, consumer services, health care, industrials, oil and gas, technology, telecommunications and utilities. We follow the same empirical specifications as the country-level splits in Table 5. Model 1 under each industry category refers to effects from firm-level determinants and model 3 is the same after including country fixed effects. Conversely, in model 2 only country fixed effect is run to isolate the country-specific effects on R&D. To conserve space, we only include R&D expenses scaled by total assets as a dependent variable. The definitions of other variables are given in Appendix 1 of this paper. We include control variables in the regression but do not report here due to paucity of space. * statistically significant at the 10 percent, ** statistically significant at the 5 percent and *** statistically significant at the 1 percent level. t-stats are given in parenthesis and are based on robust standard errors.

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Table 7: Robustness check- Using stock R&D (1) (2) (3) (4) (5) (6) (7) (8) (9)

Dep Variable Stock R&D Expenses Scaled by Total AssetStock R&D Expenses Scaled by Total

Sales Stock R&D Expenses Scaled by Net PPELog Asset -0.0267*** -0.0317*** -0.0568*** -0.0574*** -0.533*** -0.580***

(-3.11) (-3.87) (-6.97) (-7.33) (-4.05) (-4.30)PTBV 0.00540*** 0.00540*** 0.0100** 0.00932** 0.0684*** 0.0613***

(5.68) (5.63) (2.26) (2.30) (3.69) (3.73)Cash holding 0.320*** 0.305*** 1.945*** 1.957*** 10.26*** 10.23***

(8.51) (8.58) (7.70) (7.44) (8.25) (9.12)Export Intensity 0.0835*** 0.0960*** -0.0573 -0.0718 0.530 0.422

(3.38) (3.46) (-1.45) (-1.59) (1.45) (0.92)Log (1+Firm Age) 0.0300*** 0.0284*** 0.0255*** 0.0193** 0.392*** 0.355***

(4.93) (4.57) (3.19) (2.06) (3.43) (2.97)Leverage -0.0190 -0.0185 0.403*** 0.401*** 0.711* 0.545

(-1.61) (-1.40) (4.48) (3.96) (1.69) (1.31)Industry Spillover 0.677*** 0.646*** 0.524*** 0.522*** 0.570*** 0.542***

(29.43) (25.37) (12.24) (10.88) (25.85) (29.43)Competition -0.00579 0.0331** 0.201* 0.132 0.444 0.136

(-0.27) (2.45) (1.96) (1.06) (0.75) (0.20)Constant 0.288*** 0.00569 0.348*** 0.446*** -0.116*** 0.774*** 5.023*** -3.093*** 5.759***

(2.88) (0.34) (3.46) (6.04) (-3.19) (6.57) (3.73) (-5.16) (4.32)N 114,406 115,087 114,406 114,075 114,804 114,075 113,971 114,685 113,971Adj. R2 35.3% 8.8% 35.9% 23.0% 3.1% 23.1% 27.4% 6.2% 27.8%Firm Variables? Yes No Yes Yes No Yes Yes No YesCountry Fixed Effect? No Yes Yes No Yes Yes No Yes YesYear Fixed Effect? Yes Yes Yes Yes Yes Yes Yes Yes Yes

In this table, we undertake robustness checks. Here, we consider stock of R&D as the dependent variable instead of investment of R&D. We use the perpetual inventory method to construct the R&D stock variable under the assumption of 15 per cent depreciation rate following Hall (1993) and Hall et al. (2005). R&D Asset Ratio (using stock method)- We follow perpetual inventory method to construct the R&D Asset stock variable. Following Hall (1983) and Hall et al. (2005) we assume 15 per cent depreciation rate. R&D Sales Ratio (using stock method)- We follow perpetual inventory method to construct the R&D Sales stock variable. Following Hall (1983) and Hall et al. (2005) we assume 15 per cent depreciation rate. R&D Capital Ratio (using stock method)- We follow perpetual inventory method to construct the R&D Capital stock variable. Following Hall (1983) and Hall et al. (2005) we assume 15 per cent depreciation rate. The definitions of other variables are given in Appendix 1 of this paper. * statistically significant at the 10 percent, ** statistically significant at the 5 percent and *** statistically significant at the 1 percent level. t-stats are given in parenthesis and are based on robust standard errors.

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Table 8: Hierarchical Model

Sample N

Country Intraclass

Correlation Coefficient

Firm and Country Intraclass

Correlation Coefficient

Full 123,019 4.52% 69.61%ICRG High 72,806 5.06% 75.62%ICRG Low 47,955 3.29% 68.98%Intellectual Property Protection High 60,698 3.40% 77.71%Intellectual Property Protection Low 62,321 5.29% 68.62%Competitive Advantage High 84,386 4.20% 72.70%Competitive Advantage Low 38,633 3.15% 68.47%Innovation Capacity High 101,050 4.84% 73.66%Innovation Capacity Low 21,969 2.31% 60.90%Scientific Research Institution Quality High 102,078 3.76% 73.06%Scientific Research Institution Quality Low 20,941 27.91% 75.62%Overall Infrastructure Quality High 88,998 3.65% 72.49%Overall Infrastructure Quality Low 34,021 4.58% 65.37%Research & Training Services Availability High 100,999 4.77% 73.59%Research & Training Services Availability Low 22,020 20.14% 76.62%Technological Adoption High 99,160 4.43% 74.02%Technological Adoption Low 23,859 10.32% 70.39%Technological Readiness High 87,765 3.30% 70.99%Technological Readiness Low 35,254 7.59% 82.33%Innovation & Sophistication Factors High 98,948 4.83% 73.45%Innovation & Sophistication Factors Low 24,071 3.29% 63.09%Global Competitive Index High 102,266 4.04% 72.56%Global Competitive Index Low 20,753 15.85% 75.44%

In this table, we use hierarchical lineal modelling (HLM) to simultaneously estimate the effect of firm-versus-country level determinants of R&D intensity. In addition to the full sample, we use the country-level splits in Table 5 to check whether our main findings change. The definitions of other variables are given in Appendix 1 of this paper. We include control variables in the hierarchical model but do not report here due to paucity of space.

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