On the Determinants of Resource Allocation to R&D: Exploring the Potential Role of Foreign Technology Spillovers Abdoulaye Seck Faculté des Sciences Economiques et de Gestion Université Cheikh Anta Diop, Dakar January 17, 2011
Abstract Why do some countries expend signi…cant resources pursuing R&D, while others expend little? What factors determine the extent of R&D spending? This paper o¤ers some answers based on a generalized tobit model and data on nearly one hundred countries. In particular, the role of international technology spillover as a potential driving force for R&D activity is explored. Empirical results suggest that foreign R&D spillover matters only for countries that are already involved in knowledge production. Other factors that explain the extent R&D spending are human capital, knowledge accumulation through learning-by-doing, as well as the extent of intellectual property rights protection. These results add to the literature on economic divergence between advanced and poor countries, and o¤er a formal basis for designing policies aiming at reversing the divergence trend. Keywords: R&D investment, determinants, technology spillover. JEL Classi…cation Numbers: O31; O33.
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Introduction
Technology has long been seen as a major source of economic growth. How knowledge is generated has been a focus of the new growth theory developed in the early 1990s. By investing in research and development (R&D), an economy as a whole can substantially bene…t in terms of increased productivity. And yet, only a few countries around the world seem to be signi…cantly engaged in knowledge production, and to the extent that they do, they appear to be devoting fewer resources than what the very large returns should command. Author contact:
[email protected].
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In e¤ect, two main features of world R&D activity are concentration and underinvestment. Less than two thirds of countries around the world were signi…cantly engaged in technology production in 2005, and some 25 OECD countries accounted for more than 86 percent of total R&D spending.1 At the same time, the literature on technology development and di¤usion has documented remarkably high rates of return on R&D investment. For instance, Link and Siegel (2009) provide a case study on an injectable composite bone graft in which they evaluate the real internal rate of return of 230 percent as well as a bene…t-to-cost ratio of 5,400:1. More generally, Coe and Helpman (1995) suggest an average own rate of return on total R&D investment of more than 120 percent, and, interestingly enough, an additional spillover bene…t to their foreign partners of 85 percent. Comparison between the socially optimal level of R&D spending (which accounts for both domestic and foreign returns) and its actual level clearly indicates that performing countries are investing less. Some studies estimated the amount of investment that the social returns would have commanded to be at least four times larger than its actual level (see, for instance, Jones and Williams, 1998).2 If indeed investment in technology improvement is a major source of economic progress over the long run, it is then puzzling that countries appear to devote relatively small shares of their national resources to the R&D sector. This begs a study of the major forces governing the allocation of resources to knowledge production, or the factors that shape the economic and institutional incentives to which inventors and innovators respond. The economic growth literature has suggested some key factors, ranging from government subsidies (Phelps, 1966; Shell, 1966) to the appropriate opportunities to talented individuals (Baumol, 1990; Murphy et al., 1991; Acemoglu, 1995), the extent of intellectual property rights (Mazzoleni and Nelson, 1998), knowledge accumulation through learning-by-doing (Barhan, 1970), human capital accumulation (Nelson and Phelps, 1966; Englebrecht, 1997), the size of domestic markets as well as trade openness to allow for the economies of scale associated with technology to fully materialize (Grossman and Helpman, 1991). This paper sets out to explore an additional possible determinant of R&D investment in a country: foreign technology spillover. As consistently shown in the empirical growth literature, a country can bene…t substantially from its economic partners’ e¤orts to produce new knowledge, for instance by importing goods that embody new technology or by hosting foreign technologically-advanced activities (FDI) (Keller, 2004). These spillover gains, viewed as positive externalities, can provide strong incentives to develop an R&D sector. Given that countries that are actually at the technology frontier gain much more from their own R&D investment e¤ort than their partners, a country that is already gaining from the world technology might want to improve those bene…ts by starting to invest signi…cantly in knowledge production. In addition, there are many reasons to believe that countries with stronger absorption capabilities 1
Author’s calculations from UNESCO datasets. R&D personnel shows that the percentage of countries with signi…cant R&D sector is 43 percent, and R&D expenditure indicates 54 percent. Combining both indicators gives 57 percent. Sections 3 and 4 provide more discussion. 2 Hall et al. (2009) provide an extensive discussion of the di¤erent attempts to obtain both private and social returns to R&D as well as the associated measurement and econometric issues.
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are also the ones that are more likely to develop a signi…cant R&D sector. First, most of the underlying factors that govern resource allocation to R&D on one hand, and foreign technology absorption on the other hand, tend to be identical (for instance highly skilled workforce or intellectual property rights). Second, domestic companies’ interactions with more technologically-advanced …rms (through FDI for instance) more often lead them to develop at least equally e¤ective technologies in order to stand up to the sti¤er international competition. Third, as a way to adapt foreign technology to domestic conditions as well as to reverse-engineer foreign technology, a country needs to develop its own R&D sector. Fourth, if one assumes that the amount of accumulated knowledge (through spillover for instance) has a positive e¤ect on the productivity of domestic innovative activities, then the larger the knowledge stock, the easier it gets to produce technology, and the stronger will be the incentives to innovate. On the other hand, however, international technology spillover can act as disincentives for the bene…tting countries to develop a signi…cant R&D sector. In e¤ect, mostly in the context of poor countries with relatively tight …nancial constraints, lack of strong human capital, and weak institutions, investing in research could be not only too costly regardless of the type of resource one considers (dollar amount or human and physical resource), but the returns might be highly uncertain. In contrast, adopting foreign technologies through copying or imitation does not always entail similar costs. In fact, international trade makes available newly developed technologies that are embodied in imported inputs such as machinery and equipment, and o¤ers a platform for communication, learning, and imitation. In addition, technology brought by foreign companies through FDI can spill over to domestic …rms through for instance labor turnover. Other relatively costless channels through which a country can e¤ectively bene…t from new technologies are international migration and foreign technical assistance. This is generally the case for poor countries. All these channels can provide signi…cant bene…ts to a country without the costs associated with investing in research. The only cost would be to develop strong absorption capabilities of foreign technologies. Therefore, it may be more attractive to allocate (scarce) resources to the strengthening of technology absorption capacity, for instance by heavily investing in higher education, or by improving the institutional quality (e.g. property rights). Therefore, countries’ gains from foreign technology spillover may or may not provide su¢ cient incentives to develop a viable R&D sector. The incentives provided by foreign technologies came to be known as “standing-on-shoulders” e¤ect or international spillover e¤ect: past research bene…ting current or future domestic research activity. The disincentives are summarized by the so-called “raising-the-bar” e¤ect by which foreign discoveries push out the de…nition of “new”knowledge, and the “…shing-out”e¤ect that stipulates that prior discoveries are easier than the latest ones. Consequently, an empirical investigation can provide a quantitative assessment of the strength of the incentives brought about by technology spillover, as well as how countries respond to such incentives in considering whether to engage in the production of knowledge, and to the degree that they do, how strong these responses are in terms of the extent to which they spend on innovative activities. An important segment of the growth literature that confronts the endogenous growth
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theories with data has explored similar issues within the framework of “ideas” production function (Porter and Stern, 2000). One approach considers the number of patents as a proxy for knowledge, which is then related to inputs such as human capital and the accumulated stock of knowledge. Subsequent studies break down the latter into its domestic and foreign sources. One of the major shortcomings of such a methodological approach is that by considering patent citations as a proxy for knowledge, it literally assigns zero knowledge to countries without declared or known patents; these countries may choose to rely heavily on spillover or imitation as the main source of technology acquisition. Even for countries with a ‡ourishing and vibrant R&D sector, this approach systematically underestimates the stock of ideas, for some inventors may …nd more optimal not to reveal their research outcome to the market (e.g. trade secrecy, or simply non-patenting). In so doing, they avoid being imitated or copied, or triggering a response from their competitors in the form of newer, better technologies. In addition, to the extent that it accurately accounts for the stock of patents, such an approach would tell more about how productive the R&D sector is and how e¢ cient the working of the patenting system is, rather than how much of its national resource a country actually devotes to the production of ideas. This paper builds on this framework and amends it in a number of ways. The …rst and foremost is how the stock of knowledge is measured: R&D expenditure is used instead of patent citations as a proxy for knowledge ‡ow and to construct a country’s stock of domestic and foreign knowledge. Additional variables are included to account for both features of world R&D activity, that is, concentration and underinvestment. These variables include human capital, property rights, trade openness, and knowledge acquisition through learning-by-doing. A generalized tobit model, known as the two-part or hurdle model, is developed. The …rst part is a probit model, and it aims at explaining the very uneven geographic distribution of world R&D activity (i.e. why relatively few countries are engaged in technology development). The second part is a linear model, and is concerned with the extent of R&D spending for performing countries (i.e. why R&D performing countries appear to spend relatively little on technology production). The estimation results …rst indicate that international technology spillover does not provide strong incentives for countries to engage in technology development. On the other hand, for R&D performing countries, foreign R&D spillover available through the international trade channel now becomes a signi…cant factor in explaining how much they actually spend on domestic R&D production. The results also suggest that two key determinants of R&D distribution are education and intellectual property rights. Besides foreign technology gains and education, knowledge accumulation through learning-by-doing also determines the extent of R&D expenditure. The remainder of the paper is organized as follows. The next section describes the empirical approach. Section 2.3 is concerned with the description of the data and some stylized facts derived from them. Section 2.4 discusses the empirical results. Section 2.5 o¤ers a summary and some concluding remarks.
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2
Methodology
The econometric modeling starts with the ideas production function (Porter and Stern, 2000), which builds on Romer’s (1990) seminal work on endogenous technological change. The next subsection presents the model and its underlying assumptions, and o¤ers some amendments. The following subsection details the empirical strategy.
2.1
Structural Modeling
The following relation describes how technology is produced in a given country (time subscript t is dropped): :
Ai =
i HA;i Ai :
(1)
:
Ai denotes the current ‡ow of knowledge produced in country i, HA;i is the human capital input employed in the R&D sector, Ai the stock of knowledge previously acquired, i > 0 a measure of R&D productivity, and and the returns to the corresponding factors. In Romer’s (1990) theoretical framework, is set to 1, so that an increase in the amount of resources devoted to the production of ideas is translated into a proportional increase in the productivity of the R&D sector. Other works favor less strong intertemporal spillover, such as Jones (1995). Supplementing this framework with the Nelson-Phelps model (Nelson and Phelps, 1966) o¤ers a richer analysis. An important insight is that one additional role of human capital is to enable the labor force to absorb and assimilate new technology, especially from foreign countries. This view therefore calls for the need to explicitly account for the world technology frontier. It then produces the following speci…cation in which domestic and foreign knowledge sources are considered separately: :
Ai =
i HAi Ai
A i;
(2)
where A i represents technology developed in foreign countries i and then available to country i, is a measure of the extent to which domestic country i bene…ts from foreign knowledge. In particular, positive values indicate the international R&D spillover e¤ect, and negative values the raising-the-bar e¤ect. Estimates of the actual values of will help answer the central question of the paper, that is, whether technology spillovers from international transactions stimulate domestic R&D production. Furthermore, positive estimates for indicate the standing-on-shoulders e¤ect, and negative values indicate the …shing-out e¤ect. As for , values less than one indicate a possibility of duplication of ideas, which is referred to in the literature as the stepping-on-toes e¤ect. The linearized form of equation (2.2) is as follows: :
log Ai = log i + log HA;i + log Ai + log A i :
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(3)
There are many reasons to believe that the autonomous research productivity i di¤ers across countries (hence the subscript i), and these di¤erences may have to do with the various factors that shape the incentives to which inventors and innovators respond. Among the candidates are the size of the domestic market, the extent of learning-by-doing, the degree of openness to foreign transactions, and the quality of domestic institutions (e.g. property rights protection). We then assume the following: log i =
0+ 1
log CYi +
2 IP P i + 3 OPi
+
i;
where CYi represents the cumulative aggregate output of country i (which allows one to account for both the extent of technology development through learning-by-doing and the size of the domestic market), IP Pi is the strength of property rights protection (e.g. index of patent protection), OPi is openness to international trade (ratio of imports and exports to GDP), and i an error term that capture the random nature of research outcome. Substituting into equation (2.3) yields the following extension of the ideas production function: :
log Ai =
0+ 1
log CYi + 2 IP Pi + 3 OPi + log HA;i + log Ai + log A i + i :
(4)
In order to allow for the economies of scale associated with R&D activity, both domestic and foreign markets are expected to be large enough, hence a positive coe¢ cient 1 . In addition, the more a country accumulates knowledge through learning-by-doing, the easier it is expected to domestically produce new knowledge. As for intellectual property rights protection, it is less settled an issue. The literature has suggested that the e¤ect might not be always positive (Mazzoleni and Nelson, 1998). There are indeed bene…ts to providing strong protection to inventors, one of which summarized through the “invention motivation”theory, which argues that patents have the potential to increase the supply of new ideas through the market power it provides to innovators. But there are also some costs associated with patenting - one being the restrictions it brings to the access to newly developed technologies that would otherwise allow other innovators to build on them and thereby push further away the knowledge frontier. One way to test whether too much protection has the potential to actually harm the research process and thus compare between the associated bene…ts and the costs is to include a non linear functional form (i.e. quadratic) on the variable IP P . An important issue is how the ‡ows and stock of ideas are measured. Most of the literature uses patenting activity, following the direct approach (Porter and Stern, 2000). It simply counts the international patents granted to domestic innovators. A distinct inversion approach that simply derives the factor A from a Cobb-Douglas-like aggregate production function (by inverting it). As far as technology spillover is concerned, the direct approach is one of the three measurements of the extent of the R&D activity, the other two being the share of human capital devoted to the R&D sector, and R&D expenditure. It has been consistently shown that when it comes to the di¤usion of technology, the latter is more informative (Keller, 2004), some of 6
the reasons already discussed above. Adding to that our primary focus (explaining resource allocation to knowledge generation), we then amend the model by substituting investment : in R&D for patent citations. Ai is then R&D spending (RDi ), Ai domestic R&D capital stock (SiD ), and A i foreign R&D capital stock (SiF and SiM ). Domestic R&D stock of performing country j is constructed in the same way as the regular physical capital stock by using the perpetual inventory procedure: D Sjt = (1
d)S D jt 1 +RD jt ,
where d is the depreciation rate (set to 0:05). The foreign R&D capital stock for a given country is the weighted average of the domestic R&D e¤orts of its trading partners. The weights are the bilateral imports and FDI, and each one re‡ects a di¤erent di¤usion mechanism, namely the import channel and the FDI channel: SitM =
J X j=1
M Eijt
D Sjt ; Yjt
and
SitF =
J X j=1
F DIijt
D Sjt , Yjt
with SitM and SitF country i’s foreign R&D capital stocks available through imports and FDI, respectively, M Eijt is imports of machinery and equipment from R&D performing D country j, F DIijt is bilateral inbound FDI, and Yjt is GDP. The ratio Sjt =Yjt tells about the R&D intensity, that is, how much technology is embodied into a unit of country j’s output. Consequently, the foreign R&D capital indicates how much of the foreign technology country i is able to get by importing knowledge-embodied goods or by hosting foreign companies’ activity. A crucial issue is how “signi…cant”an amount of resources allocated to R&D activity is de…ned. “Insigni…cance”refers to instances in which no data on R&D inputs are reported for the entire period of time under study. The more comprehensive dataset on R&D put together by UNESCO shows many zero and missing values for a large number of countries from 1996 to 2007 and for a number of R&D indicators (like expenditures, personnel, researchers).3 While zero values are straightforward, the cases of “data not available” can be treated in many di¤erent ways. It could be because there are no good mechanisms for collecting the data, or any other reporting issues. Though this might be the case for a given year or a shorter time span, it is hard to imagine that it would be the case for the whole 12-year period covered by the institution. Instead, we favor the assumption that either the R&D sector exists, but it is not that vibrant, or the resources allocated are not signi…cant enough to be reported, or an R&D sector to speak of does not exist altogether. This combination of scenarios is favored, and for a given year, say 2005, countries for which data on either R&D spending or R&D personnel are missing for the entire period of 1996-2007 will be considered to devote insigni…cant national resources allocated to the production of knowledge. 3
Data accessible at http://stats.uis.unesco.org.
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2.2
Statistical Inference
The R&D data collected by UNESCO have the merit of covering almost the entire world (208 countries), compared to the OECD datasets that focus on some 30 advanced countries. But the relatively short time period (1996-2007), coupled with many missing values along the way, does not allow one to study the technology development over time. Therefore, a cross-sectional analysis tends to be more suitable. Because the sample is a mixture of zero and positive R&D spendings, regular OLS yields inconsistent estimates. Instead, two suitable approaches have been suggested for these censored data. The …rst is the regular tobit model (referred to as type-I tobit). It assumes that the same probability mechanism generates both the zero and positive values of the dependent variable. It also relies crucially on the assumptions that the regression error is normally distributed and that its variance does not vary across observations (i.e. homoskedasticity). Because these two hypotheses are very often violated in actual applications, leading to inconsistent estimates, a second set of generalized, more ‡exible approaches have been developed. The hurdle model, also known as the two-part model, suggests that the decision to spend on R&D and the amount spent need to be modeled separately, and this approach is less vulnerable to the violations of the normality and homoskedasticity assumptions. Although many applications have shown that this two-part modeling improves the …t over the regular tobit, its validity heavily relies on the independence of these two mechanisms. The violation of this assumption causes a selection bias that plagues the results of the second-step regression. An alternative approach, known as the sample selection model, is based on the joint distribution for the censoring mechanism and outcome and the implied distribution conditional on the observed outcome. The hurdle model turns out to be the most appropriate approach in the context of the dataset used in this paper. In e¤ect, the data reject the assumption of normality, making the type-I tobit maximum likelihood inconsistent. On the other hand, there is no evidence for the superiority of the type-II tobit (selection model) since the assumption of independence of the mechanisms that generate zero and positive R&D spending.4 Let the latent (unobserved) variable y denote the logarithm of R&D spending. y is not translated into actual spending y until some threshold L(= 0) is passed.5 Then comes the following observation rule:6 y=
y if ln y > L 0 if ln y L
Next, let d denote a binary indicator so that y = 0 when d = 0, and y = y > 0 when 4
The appendix o¤ers detailed discussion of the econometrics of censored data, and shows how various test procedures indicate that the hurdle model is the most appropriate modeling approach for the data used in this paper. 5 Both y and ln y have the same left cut-o¤ point of 0, because none of the values of y is equal or below one. In other words, the censoring structure is the same. : 6 The latent variable is the ‡ow of new knowledge, that is, y = A.
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d = 1. The two-part conditional density of y is given by the following expression: f (yjX) =
Pr(d = 0jX) if y = 0 ; Pr(d = 1jX) f (yjd = 1; X) if y > 0
where X represents the same set of regressors in both parts of the models (no external exclusion restriction is included). As in the case of instrumental variables, …nding a variable that exerts a signi…cant impact on the likelihood to spend but has no impact on the amount spent is a formidable task. Instead, the identi…cation issue associated with the exclusion restriction is assumed to be taken into account by the non-linearity brought about by the probit model in the …rst part (this is referred to as “identi…cation through non-linear functional form”). Using the probit model in the …rst part and the lognormal model for y conditional on positive R&D spending in the second part, it then comes the following conditional mean to be estimated: E(yjX) = (X 0
exp(X 0
1)
2
+
2
=2);
with the standard normal cumulative density function, 1 and 2 the parameters in the likelihood equation and the linear equation, respectively. A maximum likelihood techniques has the ability to estimate the parameters of the probit model using all the observations, and the parameters of the density f (yjd = 1; X) using only the uncensored observations.
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Data
The main R&D data source is UNESCO. It provides information such as annual R&D spending and R&D personnel. As discussed above, whether a country is considered to have been investing signi…cantly in knowledge production relies on the availability of data for at least one year over the entire time period and for either R&D spending or R&D personnel (or both). As far as R&D spending is concerned, there are 112 R&D performers out of the 208 countries covered by the institution, representing 54 percent. Data on R&D personnel indicated lower …gures: 89 countries, or 43 percent. The combination of both R&D activity indicators reveals that 119 countries around the world have been signi…cantly engaged in R&D production, or 57 percent. Going from the raw data to a workable sample to be used in the regression analysis is a challenging task that requires some fairly strong assumptions. The most recent year with the largest number of available data on R&D spending is 2005, with 88 data values. For 22 countries, the data for 2005 can be extrapolated relatively easily, either by using the average values between 2004 and 2006 (Australia, Malaysia, and Sri Lanka), or by applying to the 2004 or 2003 available values the average growth rate of R&D spending computed over the previous years (19 countries). For the nine R&D performing countries left, existing data do not allow to apply extrapolation. Instead, we look at the other variables included in the analysis, especially education and the index of patent protection, to see whether data are available for these countries. It turned out that for …ve of them, there exist no such data. They are eliminated from the …nal sample, following the very same process 9
by which regression techniques ignore observations with missing data for any independent or dependent variables. As for the remaining four countries (El Salvador, Jordan, Mozambique, and Nepal), for which data on education and patent protection do exist for the year 2005, it is assumed that dropping them from the analysis will not signi…cantly a¤ect the main regression outcomes. With these assumptions dictated by data constraints, the …nal sample we consider for various statistical and econometric analyses consists of 98 countries. An important sampling issue is whether the working sample is representative of the population it is drawn from. Aside the selection problem that might be dealt with formally, some desirable properties for a sample have to do with its ability to re‡ect the main facts from the original source. As far as R&D is concerned, it turns out to be the case. For instance, the original sample of 208 countries shows a proportion of 0.57 of R&D performing countries, and the working sample of 98 countries a proportion of 0.66. The corresponding margins of error of 0.067 and 0.094 make these two proportions statistically not di¤erent from one another (the con…dence intervals do intersect). In addition, the average amount spent on R&D is $14.1 billion per country in the original sample, and is statistically close to $14.3 billion in the reduced sample, the …gures for R&D spending per capita are respectively $2427 and $2465. Table 1 o¤ers some summary statistics. The total R&D spending of $14.3 billion (in 2005 US$ PPP) represents 1.07 percent of GDP, which is by comparison far below the average physical investment ratio of 21.5 percent. In level terms, the countries that top the list of R&D investors are the US ($324 billion), Japan ($129 billion), China ($71 billion), and Germany ($62 billion). But in terms of investment e¤ort measured by per capita spending, which also indicates the R&D intensity, the relatively large size of the population in these four countries (which is also translated into their labor force) make them lose the top spot. Germany now ranks 31st with $1173.6, Japan 34th with $1002.4, US 38th with $651.1, and China 64th with $67.8. Countries that appear to be devoting much e¤ort in developing R&D according to per capita expenditures are Singapore ($6773.2), Finland ($6510.6), Norway ($5578.4), and Denmark ($5346.2). Longer time series data on R&D spending from OECD are used to construct domestic and foreign R&D capital stocks. Larger foreign technology capital in R&D performing countries is an indication of the stronger economic ties among them, in terms of trade in (knowledge-embodied) goods and capital ‡ows. The …gures show that, in addition to the many economic advantages associated with actually developing technology domestically, these R&D performing countries bene…t more from foreign technology than countries that do not invest signi…cantly in R&D. Furthermore, their far largest cumulative GDP tells about how much they may also bene…t from another source of technology development, that is, learning-by-doing. This technology leadership of the R&D performing countries is associated with more openness to trade, which o¤ers a greater access to larger markets, necessary to fully bene…t from any economies of scale. In addition, these technology leaders enjoy a larger stock of human capital as well as stronger property rights, which are two important ingredients in inventive and innovative activities. In e¤ect, human capital is a center-piece in most of the
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Table 1: Summary statistics, 2005. R&D activity R&D spending, total* R&D spending p.c. S M p.c. S F p.c. Cumulative GDP p.c. Education Property rights Openness Count
Performing 14.32 2465.02 16.26 13.62 236.01 7.3 4.3 93.0 65
Non-perfor. 0.0 0.0 5.34 4.84 88.05 3.4 3.7 78.2 33
Sample 9.50 1634.96 12.58 10.66 186.19 6.3 4.0 88.1 98
Notes: S M and S F represent foreign R&D capital stock available through imports and FDI, respectively. “p.c.” stands for “per capita” (divided by labor force). * denotes $US billions. Source: UNESCO.
growth theories that endogenize the dynamics of technology. It is generally modeled not only as an input in the production of regular goods and services or in the production of knowledge, but also as an important determinant of knowledge di¤usion and adoption (see, for instance, Nelson and Phelps, 1966). As for property rights protection, it has the potential for guaranteeing some market power to innovators, and the resulting high returns tend to act as an incentive to investing in ideas production. Whether these stylized facts are actually translated into signi…cant in‡uences on the both the decision to spend and the amount spent on R&D is ultimately an econometric issue. The next section presents and discusses the empirical results.
4
Estimation Results
Table 2 provides the estimation results for the hurdle model. The …rst part (probit model) is concerned with the question of world R&D distribution. It is given by equation (2.4) in which the dependent variable is 1 for countries that are spending on R&D and 0 otherwise. The second part (linear model) looks at the determinants of R&D spending in performing countries. It is also given by equation (2.4), and the dependent variable is the log of R&D spending. Both models are estimated in per capita terms.7 When it comes to R&D distribution around the world, the results unequivocally suggest that neither import- nor FDI-related technology spillovers have a signi…cant e¤ect on whether a country engages or not in the production of knowledge. In fact, no matter how much technology spills over from foreign to domestic countries, international R&D spillover does not provide the latter with strong enough incentives to actually allocate a signi…cant share of 7
So that, like human capital, variables such as R&D spending, foreign R&D stocks, and cumulative GDP are scaled by the labor force.
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their national resources to the development of a viable R&D sector. But for countries that are actually spending on R&D, foreign technology spillover exerts a signi…cant in‡uence on the extent of the R&D sector development. In e¤ect, through the import channel, a one-percent increase in foreign R&D capital stock is translated into a 0.37 percentage point increase in R&D spending per capita. This suggests that the positive international-spillover e¤ect dominates the negative raising-the-bar e¤ect. On the other hand, no signi…cant (net) gain is associated with the FDI channel, which indicates that the two opposing e¤ects, if they exist, are of equal magnitude (in absolute terms). These results are di¤erent from those found by Porter and Stern (2000). In e¤ect, the authors’direct approach applied to R&D performing countries suggests that domesticallyproduced ideas are declining in the worldwide stock of ideas, suggesting that foreign discoveries raise the bar for producing new knowledge in domestic countries. Di¤erences may be primarily due to the approaches used to proxiing knowledge ‡ows and stocks (patent citations against R&D spending). It may be reasonable to expect new foreign patented innovation to make it harder for domestic innovators to produce newer discoveries than it would otherwise. But as far as R&D spending is concerned, new foreign discoveries may not in fact discourage domestic innovators to engage further in ideas production. Instead, the discovery process, which is now getting harder as a result of new foreign discoveries, would mean that more resources and e¤ort are needed to generate truly new ideas. In addition, more availability of foreign knowledge could further help the discovery process if it is assumes that knowledge builds on knowledge. These two e¤ects can explain both the positive direction and the magnitude of the e¤ect of international technology spillover on domestic R&D spending. The results also indicate that knowledge gained through learning-by-doing matter only if a country is already engaged in technology development: a one-percent increase in the knowledge stock accumulated through this process is translated into a 0.377-percent increase in per capita R&D spending. Knowledge acquired through this channel is therefore important only to the extent that it can be used as an input to a more formal production of technology through an R&D sector. Furthermore, cumulative GDP is also a proxy for market size, and the positive result is therefore an indication of the strong incentives the latter brings about, that is, economies of scale needed to maximize the returns to invention and innovation. The insigni…cant coe¢ cient on the trade openness variable, which helps capture the extent of the access to foreign markets, could mean that what matters the most for domestic R&D activity is domestic markets; the access of the latter does always not su¤er from the many impediments that are often associated with trade across borders. A crucial factor that explains both the decision to expend signi…cant amount pursuing R&D and the extent of R&D spending is the intellectual property rights protection. In e¤ect, the results suggest that some form of patent protection is needed to engage a country in knowledge production, as indicated in the …rst part model. But once a viable R&D sector is developed, further strengthening of property rights actually ends up harming the discovery and innovation process, as suggested by the negative coe¢ cient estimates in the second part model. The literature that looks at the e¤ect of patent protection of R&D indicates that
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Table 2: Estimation results of the hurdle model.
log (S M p:c:) log (S F p:c:) log (CumGDP p:c:) log (Edu:) IP P Openness Intercept N Log Likelihood Pseudo-R2 Adj-R2
Part 1: Probit Coe¢ cients Marginal e¤. 0.0888 0.0197 (0.626) (0.136) -0.2580 -0.0571 (0.580) (0.121) 0.5951 0.1316 (0.998) (0.214) 2.3940** 0.5295* (1.209) (0.316) 5.5940** 1.2372** (2.634) (0.599) 0.0007 0.0001 (0.006) (0.001) -6.5948 (3.780) 86 -30.08 0.385
Part 2: Linear Coe¢ cients 0.3662*** (0.111) -0.0075 (0.121) 0.3770* (0.195) 0.4342*** (0.109) -1.7993** (0.722) 0.0005 (0.001) 0.6831 (0.642) 64
0.422
Notes: The dependent variable is log R&D spending per capita (p.c.) which is censored for countries that are not signi…cantly engaged in technology production. In the probit model, it takes the value of 1 for countries spending on R&D and 0 otherwise; and in the linear model, it is the log of R&D spending for performing countries, the other countries being dropped. The standard errors are in parentheses, and signi…cance at 1, 5, and 10 percent are indicated by ***, **, and *. The domestic R&D capital is dropped as an explanatory variable because, as a censored variable, it has many zero values, even for some countries that are spending on R&D. This is due to the lack of long enough time series data.
13
an e¢ cient system should de…nitely allow innovators to bene…t from and build on prior research proceedings, while at the same time guaranteeing a strong basis to collect higher returns. More speci…cally, such a system has a tendency to provide su¢ cient motivation for useful invention (in line with the “invention motivation” theory), or to induce inventors to disseminate their discoveries (the “invention dissemination” theory), hence allowing the exploration of broader prospects of the discovery (the “exploration control”theory).8 Our empirical results could suggest that the motivation associated with patent protection is important at early stages of technology development when some countries embark into a path that leads to the emergence of a signi…cant R&D sector while others fail to do so. But for R&D performing countries, it could be that stronger protection might prevent the invention dissemination e¤ect and the exploration control e¤ect from materializing signi…cantly. In e¤ect, too strong a patent protection tends to reduce the pace of knowledge production, dissemination, and di¤usion, hence preventing other innovators from building upon new research proceedings that would otherwise have contributed to further push away the technology frontier. In practice, it has always been challenging to design a balanced patent structure that e¤ectively reconciles private and social returns, and the econometric results can be viewed as an indication that as the protection scheme gets stronger, so too the challenge gets more complex. As one would expect, education plays a signi…cant role in both the decision to engage in technology production, and the extent of the R&D sector development for performing countries. On average, the marginal e¤ect suggests that a one-percent increase in the average years of schooling is translated into more than half a percentage point increase in the probability that a country engages in knowledge production. As for countries that are already spending signi…cantly on R&D, a one-percent increase in human capital leads to a 0.43-percent increase in R&D spending. This is in line with the …ndings by Porter and Stern (2000), despite the di¤erence in the approaches. The signi…cant role of education is also in line with various theoretical representations that place human capital at the heart of knowledge production (for instance, the endogenous growth models), or those that consider it as one of the main forces that underlie countries’ ability to absorb and assimilate new technology (e.g. Nelson and Phelps, 1966), or even those that simply view human capital accumulation as another form of knowledge production that occurs outside the R&D sector (Engelbrecht, 1997). However, the concave shape of the relationship between domestic ideas productivity and human capital is an indication of the stepping-on-toes e¤ect, implying the existence of the duplication of ideas. Overall, an important implication in terms of domestic and international cooperation among innovators can be drawn. International technology spillover spells some form of complementarity between domestic and foreign R&D activities. But the evidence on the duplication of ideas and the negative e¤ect of strong property rights can be an indication of some lack of cooperation among domestic innovators. This calls for the need to design a sound property rights system that would provide strong motivation in order to get many countries that lag far behind the technology frontier to actually start expending signi…cant 8
These di¤erent concepts were coined by Mazzoleni and Nelson (1998).
14
resources on R&D on one hand, and contribute to the dissemination of newly discovered ideas that could be used as an input in current and future research activity on the other hand.
5
Summary and Concluding Remarks
The aim of this paper was to understand the process of knowledge generation. In particular, the role of foreign knowledge spillover is explored as a potential factor explaining two main patterns of the distribution and the extent of world R&D activity, namely concentration and underinvestment. The results from a generalized tobit model suggest that foreign technology spillover matters only when a country is willing to develop a signi…cant R&D sector. In e¤ect, neither import- nor FDI-related technology spillover is a signi…cant discriminant between countries that are expending signi…cant resources pursuing R&D and those that are not. But for R&D performing countries, foreign technology available through the import channel matters, and the corresponding positive e¤ect is an indication that the international spillover e¤ect dominates the raising-the-bar e¤ect.9 The results also suggest that the level of human capital is crucial for both the decision to spend and the amount spent on R&D, and the magnitude of the coe¢ cient estimate suggests that there is no signi…cant duplication of ideas within the domestic R&D sector. In addition, the amount of accumulated knowledge through learning-by-doing is important only for countries that are already signi…cantly involved in R&D. Furthermore, property rights protection matters only to the extent that it provides some market power to potential innovators at an early stage of an R&D sector development. But along this development process, too strong a protection prevent other innovators from using existing research proceedings, resulting in a situation in which these negative e¤ects counteract positive motivation e¤ect. The results may o¤er additional insights into cross-country growth dynamics. Economic divergence between advanced and poor economies has been signi…cantly documented (see, for instance, Pritchett, 1997). As suggested by the endogenous growth theory, a country can sustain an ever-continuing rise in its standards of living by investing in new technology. Therefore, reversing the divergence trend would require changing mostly one pattern of world R&D activity, that is, the relative concentration within the developed world. Factors that a¤ect the likelihood of a country deciding to engage in the production of ideas on one hand, and those that explain the R&D investment e¤ort by performing countries on the other, could o¤er a basis for strategies seeking growth convergence between advanced and poor economies. 9
A good summary would be “fuel is important only when there is a …re that needs to keep burning.”
15
Appendix A
Detailed Estimation Results
This part o¤ers additional insights into the estimation strategies applied to the speci…c data used in the second part of the dissertation. The mixture of censored (zero) and uncensored (positive) observations requires particular econometric treatments. The standard approach is the tobit model, which helps explain both the decision to spend and the amount spent on R&D. The assumptions on which the validity of the model relies are normality and homoskedasticity. R&D spending per capita tends to violate the normality assumption: it is heavily skewed (S = 4:6) and has a large non-normal kurtosis (K = 26:9). A lognormal for this expenditure variable may be more appropriate: the corresponding skewness (-0.4) and kurtosis (1.5) make the variable in log close to normality. This reinforces the validity of the choice of the logarithmic form in the linearized ideas production function (see equation (3.3)). The latent variable y (log of R&D spending) is not translated into actual spending y until some threshold L(= 0) is passed.10 This is expressed into the following expression: y=
y if ln y > L 0 if ln y L 0
By considering the following regression equation: yi = exp(Xi + "i ), it then comes the censored mean : 2
E(yjX) = exp X 0 +
1
2
L
X0
2
:
The truncated mean is de…ned as follows: E(yjX; y > 0) = E(yjX)= 1
L
X0
2
;
with (:) the standard normal cumulative density function, and "i assumed to be normally distributed with zero mean and a constant variance 2 across individuals. Under these assumptions, the tobit maximum likelihood estimator generates the results shown in Table 3. Foreign R&D appears to exert no signi…cant e¤ect on both the decision to spend and the amount spent on R&D. The only factors that matter are human capital and the extent of property rights protection. As for functional form of the latter, the linear one appears to be supported by the data over the non-linear one, indicating that further strengthening has always a positive e¤ect on both the likelihood of R&D spending and the extent of the spending. 10
Both y and ln y have the same left cut-o¤ point of 0, because none of the values of y is equal or below one. In other words, the censoring structure is the same in both level and logs.
16
Table 3: Tobit maximum likelihood estimation results. M
log (S _p:c:) log (S F _p:c:) log (CumGDP _p:c:) log Education IP P
(I) 0.2966 (0.383) -0.0156 (0.369) 0.2487 (0.634) 2.8893** (1.293) 3.7642* (2.048)
IP P _square Openness Intercept N Uncensored Left-censored Sigma Log likelihood Pseudo-R2
0.0012 (0.003) -4.3404 (2.187) 86 64 22 1.164 -120.62 0.213
(II) 0.3562 (0.380) -0.0933 (0.371) 0.4905 (0.643) 2.9107** (1.275) 21.2657* (11.124) -18.4861 (11.385) 0.0010 (0.003) -9.4286 (3.869) 86 64 22 1.147 -119.22 0.223
Notes: The censored dependent variable is the logarithm of R&D spending per capita for countries that are signi…cantly engaged in technology production, and 0 for the rest. IP P represents the index of patent protection. Speci…cation (II) explores a quadratic form for the variable IP P . The standard errors are in parentheses, and signi…cance at 5 and 10 percent are indicated by ** and *.
The appropriateness of the tobit maximum likelihood estimates rests on the validity of the assumption about the conditional data distribution. The Lagrange multiplier or score tests of homoskedasticity and normality strongly reject these two hypotheses: the corresponding highly signi…cant test statistics are respectively 63.35 and 63.41. A …rst generalization of the tobit model is the two-part model also known as the hurdle model. It relaxes the strong assumption about normality and homoskedasticity. It also relaxes, although partially, the conditional data distribution. It assumes that the choice to either develop an R&D sector or not, on one hand, and, conditional on a positive outcome, the amount actually spent on the other hand, are generated by two di¤erent and independent mechanisms. It then requires running separately a probabilistic model (e.g. probit) explaining the decision to engage in R&D development, and a linear model looking into the factors a¤ecting the extent of R&D spending for performing countries. The estimation results are 17
Table 4: Estimation results of the hurdle model. (I) M
log (S _p:c:) log (S F _p:c:) log (CumGDP _p:c:) log Education IP P
(II)
Part 1 0.0888 (0.626) -0.2580 (0.580) 0.5951 (0.998) 2.3940** (1.209) 5.5940** (2.634)
Part 2 0.3662*** (0.111) -0.0075 (0.121) 0.3770* (0.195) 0.4342*** (0.109) -1.7993** (0.722)
0.0007 (0.006) -6.5948 (3.780) 86 -30.08 0.385
0.0005 (0.001) 0.6831 (0.642) 64
IP P _square Openness Intercept N Log Likelihood Pseudo-R2 Adj-R2
0.422
Part 1 0.1280 (0.637) -0.2702 (0.566) 0.5281 (1.014) 2.3394* (1.245) -7.0875 (6.391) 5.7073 (4.100) 0.0001 (0.006) -3.3856 (2.371) 86 -29.75 0.392
Part 2 0.4161*** (0.105) -0.0592 (0.143) 0.4582** (0.185) 0.4454** (0.178) 9.8614** (4.045) -8.4506 (6.916) 0.0005 (0.001) -2.5947 (1.273) 64
0.454
Notes: The dependent variable is a dummy that takes the value of 1 for countries that are engaged in R&D production and 0 otherwise in Part 1 (probit model), and the logarithm of R&D spending per capita for countries that are signi…cantly engaged in technology production in Part 2 (linear model). The standard errors are in parentheses, and signi…cance at 1, 5, 10 percent are indicated by ***, **, *.
shown in Table 4, where the …rst part is a probit likelihood model, and the second part a simple linear model for R&D performing countries only. As before, foreign R&D has no signi…cant in‡uence on the decision to spend on R&D, but for countries that are already spending on R&D, it has a signi…cant positive e¤ect through the import channel. Education has a positive in‡uence on both the decision to spend and the amount spent. Property rights protection has a positive e¤ect on the likelihood that a country engages in innovative activity, but once it does, the e¤ect becomes negative. This type of non-linearity is preferred over the one that consists of including a quadratic form, and it indicates that some form of patent protection is important to motivate innovators, but once they start producing knowledge, further strengthening does not help the innovation process. As for knowledge accumulated through learning-by-doing, it matters only for countries that are already spending on technology development. The joint likelihood for the two-part model is -41.82 (speci…cation (I)), which is the sum 18
of the two log likelihood of the probit and linear estimations, because of the independence assumption. It is larger than the log likelihood of the Tobit model (-120.62). The two-part model therefore provides a much better …t to the data, despite the penalty brought about by the increased number of regressors. As for the twin problems of heteroskedasticity and non-normality, the model eliminates the former, but not the latter. In e¤ect, the Breusch-Pagan/Cook-Weisberg insigni…cant test statistic of 0.25 indicates a failure to reject the null of homoskedasticity, and the joint skewness/kurtosis test for normality shows a signi…cant test statistic of 11.79, rejecting the null of normality. But unlike in the tobit model, the non-normality does not bring inconsistency in the estimation results. A more serious concern is the independence assumption. If, after controlling for the relevant factors, country selection into R&D activity is not random, then the second-stage regression results will su¤er from selection bias. The Heckman sample selection model, also known as type-II tobit model, allows for the possibility that the mechanisms that generates the zero and the positive values to be dependent, and yet very di¤erent. The results are shown in Table 5. The log likelihood of the selection model is lower than that of the hurdle model (-46.49 against -41.82). In line with that is the estimated value of the correlation coe¢ cient of the errors (b = 0:029), strongly suggesting that the hypothesis that the two parts of the hurdle model are independent cannot be rejected. This conclusion holds even if the bivariate normality assumption is substituted for a univariate normality one which requires running the model in two steps (the corresponding b is close to one, and lambda is insigni…cantly di¤erent from zero). Therefore the results from the two-part model have to be favored over those from the sample selection model. The appropriateness of the two-step model can also be illustrated by comparing the within-sample conditional prediction. Table 6 shows the actual and predicted conditional means, as well as their ratios. The tobit model provides very poor predictions, whether the whole sample is considered - E(yjX) - or only the R&D performing countries - E(yjX; y > 0). By contrast, the hurdle model and the selection model predict fairly well the means. Quantitatively, the predictions from the two-part model are closer to the actual mean, which further justi…es the choice of this regression method.
B
Data Sources
Data on R&D spending come from two main sources: OECD and UNESCO. The …rst source is focused on developing countries and provides long time series data, and is used to construct R&D capital stocks. The second source is of a shorter time span (starting from 1996) and very scant too, but includes many more countries (virtually the whole world). It is used for the regression analyses. Bilateral trade and FDI (outward for advanced countries) are from OECD as well. Macroeconomic data such as GDP, total imports and exports, labor force are from the World Bank World Development Indicators. Data on patent rights are from Park (2008), and data on average years of schooling from Barro and Lee (2010).
19
Table 5: Estimation results of the selection model (type-II tobit.)
log (S M _p:c:) log (S F _p:c:) log (CumGDP _p:c:) log Education IP P
(I) Selection Outcome 0.0888 0.3871** (0.616) (0.160) -0.2580 -0.0138 (0.560) (0.156) 0.5951** 0.3590** (0.298) (0.176) 2.3940* 0.4872** (1.229) (0.239) 5.5940** -2.7527** (2.634) (1.293)
IP P _square Openness
0.0007 (0.006) -6.5948 (3.780)
Intercept N Uncensored Left-censored Sigma Rho Log Likelihood
0.0004 (0.001) 1.8448 (1.399)
86 64 22 0.46 -1.25 -46.49
(II) Selection Outcome 0.1280 0.4128*** (0.637) (0.125) -0.2702 -0.0692 (0.566) (0.124) 0.5281** 0.4855*** (0.234) (0.162) 2.3394** 0.5273* (1.185) (0.317) 8.0875 13.6962** (17.391) (5.643) -15.7073 -14.3853*** (20.100) (5.040) 0.0001 0.0005 (0.006) (0.001) -3.3856 -4.3571 (5.371) (2.379) 86 64 22 0.36 1.11 -45.37
Notes: The censored dependent variable is the logarithm of R&D spending per capita for countries that are signi…cantly engaged in technology production, and 0 for the rest. The standard errors are in parentheses, and signi…cance at 1, 5, 10 percent are indicated by ***, **, *.
Table 6: Conditional mean predictions. Models y (sample mean) E(yjX) (predicted mean) E(yjX)/y (ratio) y; y > 0 (truncated mean) E(yjX; y > 0) (predicted mean) E(yjX; y > 0)/(y; y > 0) (ratio)
Tobit 1634.9 (374.3) 20.2 (2.7) 0.01 2465.0 (437.7) 26.7 (2.8) 0.01
Note: The standard deviations are shown in parentheses.
20
Hurdle 1634.9 (374.3) 1574.6 (197.6) 0.96 2465.0 (437.7) 2381.4 (208.2) 0.97
Sample selection 1634.9 (374.3) 1905.0 (251.9) 1.17 2465.0 (437.7) 2555.1 (268.2) 1.04
Table 7: Country list. Algeria* Argentina* Australia* Austria* Bangladesh Belgium* Benin Bolivia* Brazil* Burkina Faso* Burundi Cameroon Canada* Central Afr. Rep. Chad Chile* China* Colombia* Comoros Congo, Dem. Rep.* Congo, Rep.* Costa Rica* Cote d’Ivoire Denmark* Dominican Rep.
Ecuador* Egypt, Arab Rep.* El Salvador* Equatorial Guinea Fiji Finland* France* Gabon Gambia Germany* Ghana Greece* Guatemala* Guinea Guinea-Bissau Guyana Haiti Honduras* Hong Kong, China* Iceland* India* Indonesia* Ireland* Italy* Japan*
Jordan* Kenya Korea,Rep.* Malawi Malaysia* Mali Malta* Mauritania Mauritius* Mexico* Morocco* Myanmar Nepal Netherlands* New Zealand* Niger Nigeria Norway* Oman Pakistan* Panama* Papua N. Guinea Paraguay* Peru* Philippines*
Portugal* Rwanda Senegal* Seychelles* Sierra Leone Singapore* Spain* Sri Lanka* Sudan* Sweden* Switzerland* Syrian Arab Rep. Thailand* Togo* Trinidad and Tob.* Turkey* Uganda* United Kingdom* United States* Uruguay* Venezuela, R.B. Zambia* Zimbabwe
Note: * denotes countries that were spending signi…cant amounts pursuing R&D in 2005.
C
List of Countries
Table 7 above shows the list of countries used in the regression analyses, both R&D performing ones and the rest, for the year 2005. As stated before, this is the year in which most data on R&D have been collected by UNESCO.
21
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