metadata: authors: 0: author: Lederman, Daniel
1: author: Maloney, William F.
docna: 0: docna: Research and development (R&D) and development
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Social Policy,Legal Products,Real & Intellectual Property Law,Judicial System Reform,Industrial Economics,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Common Property Resource Development,Coastal and Marine Resources,Economic Growth,Legal Reform,Legislation,Regulatory Regimes,Energy and Natural Resources,Business Cycles and Stabilization Policies,Global Environment,Intellectual Property Rights
teratopic: Education
abstracts: cdata!: Lederman and Maloney trace the evolution
            of research and development (R&D) expenditures along the
            development process using a new global panel data set. They
            show that R&D effort measured as a share of GDP rises
            with development at an increasing rate. The authors examine
            how four groups of countries from Latin America, Asia,
            advanced manufacturing exporters, and advanced natural
            resource-abundant countries fare relative to the predicted
            development trajectory. Latin America generally
            underperforms as do some countries in Asia and Europe, but
            their striking finding is that some-Finland, Israel, the
            Republic of Korea, and Taiwan (China)-have radically
            deviated from the predicted trajectory and displayed
            impressive R&D takeoffs. The authors ask whether these
            countries overinvest in R&D but find that the high
            estimates of the social rates of return probably justify
            this effort. Moreover, the returns to R&D decline with
            per capita GDP. The authors attempt to explain why rich
            countries invest more in R&D than poor countries. They
            conclude that financial depth, protection of intellectual
            property rights, government capacity to mobilize resources,
            and the quality of research institutions are the main
            reasons why R&D efforts rise with the level of development.
repnme: repnme: Research and development (R&D) and development
display_title: Research and development (R&D)
            and development
listing_relative_url: /research/2003/04/2323014/research-development-rd-development
topicv3: Economic Growth m1326241 471,Natural Resources m1326446 1979,Manufacturing Sectors m1326521 1281,Patents m1326812 1041,Environment, Natural Resources and Blue
            Economy m1327391 566
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Education
origu: Chief Economist (LCRCE)
no_of_pages: 44
model_extractions: POLICY RESEARCH' WORKING PAPER 3024

# Abstract

> Lederman and Maloney trace the evolution of research and development (R&D) expenditures along the development process using a new global panel data set.

> They show that R&D effort measured as a share of GDP

> rises with development at an increasing rate. The authors

> examine how four groups of countries from Latin America, Asia, advanced manufacturing exporters, and

> advanced natural resource-abundant countries fare

> relative to the predicted development trajectory. Latin America generally underperforms as do some countries

> **in** Asia and Europe, but their striking finding **is** that some-Finland, Israel, the Republic of Korea, and

> Taiwan (China)-have radically deviated from the predicted trajectory and displayed impressive R&D

> takeoffs. The authors ask whether these countries overinvest in R&D but find that the high estimates of the

> social rates of return probably justify this effort. Moreover, the returns to R&D decline with per capita

> GDP. The authors attempt to explain why rich countries

> invest more in R&D than poor countries. They conclude that financial depth, protection of intellectual property rights, government capacity to mobilize resources, and the quality of research institutions are the main reasons

> why R&D efforts rise with the level of development.

> This paper-a product of the Regional Studies Program, Office of the Chief Economist, Latin America and the Caribbean Region-is part of a larger effort in the region to understand the role of innovation in development. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Patricia Soto, room

> 18-018, telephone 202-473-7892, fax 202-522-7528, email address psoto@worldbank.org. Policy Research Working

> Papers are also posted on theWeb at http://econ.worldbank.org. Theauthorsmaybecontactedatdlederman@worldbank.org or wmaloney@worldbank.org. April 2003. (38 pages)

> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is toget the findings out quickly, even if the presentations are less than fully polished. The_

> _papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent_

Produced by the Research Advisory Staff
Please do not cite without authors' permission.

# R&D and Development

Daniel Lederman and William F. Maloney' Office of the Chief Economist Latin America and Caribbean World Bank 'We have benefited from the stellar assistance of<sup>MarianoBosch and LauraSaenzandfrom theinvaluable</sup> discussions with Luis Miguel Benavente,<sup>Guillermo Perry, AndresRodriguez-Clare,Luis Serven and Hong</sup> Tan.
I

------

---

I
# I. **Introduction**

'The literature suggests that roughly<sup>half of cross-countrydifferencesin per capita</sup> income and growth are driven by<sup>differencesinTotalFactorProductivity,generally</sup> associated with technological<sup>progress.2Thisfact movestocenterstageanabiding</sup> question in economic development:<sup>whydodevelopingcountries,withgreatpotential</sup> gains from adopting technologies<sup>from theindustrialized countries,failto do so?Infact,</sup> perversely, the countries generating<sup>newtechnologiesat thefrontierappear to have faster</sup> TFP growth in manufacturing<sup>andagriculturethanthepoorcountrieswhocould,in</sup> theory, simply adopt.<sup>3</sup> However, recent work in innovation<sup>stressesthatadoptingexistingtechnologyis</sup> not without cost. Firms and countries need to<sup>developan"absorptive"or"national</sup> learning" capacity which, in<sup>turnarehypothesizedtobe functionsofspendingon</sup> research and development (R&D).<sup>4</sup> Though often considered relevant<sup>only forbasic</sup> science, Cohen and Levinthal (1991)<sup>amongothersstresslearning--knowingwherethe</sup> frontier is and figuring out what<sup>adaptationsarenecessary--asthe"secondface"of</sup> R&D. In fact, Pavitt (2001) argues<sup>that investmentin pure research is alsoimportant for</sup> developing countries. First, those<sup>most familiarwiththefrontiersof basic science will</sup> best train the applied problem solvers<sup>inthe privatesector.Second,evenbasicresearch</sup> does not flow easily or costlessly<sup>acrossborderssodevelopingcountries cannotsimply</sup> rely on what is being generated<sup>in theadvancedcountries.</sup> This paper investigates three outstanding<sup>issuesthatarecentraltounderstanding</sup> the links between innovation and<sup>development.We firstgenerate stylized patternsof the</sup> evolution of R&D spending over<sup>the courseof developmentemployinganewpaneldata</sup> set- constructed by Lederman and<sup>Saenz(2003).Theevidenceshowsthat R&Drises</sup> exponentially with the level of<sup>development measuredby GDP per capita.Provocatively,</sup> we also identify several striking<sup>outlierssuchas TaiwanandKorea inEast Asia,Finland</sup>

> **2** See Hall and Jones (1999), Dollar<sup>andWolf (1997).</sup>

> 3 Martin and Mitra (2001)

> _4At_ the firm level, see Cohen and<sup>Levinthal (1990), Forbesand Wield (2000), Griffith,Redding and Van</sup> Reenen (2003), Pavitt (2001) at<sup>the national level see,for example, Baumol, Nelsonand Wolf (1994).</sup>

2
and Israel among the industrialized countries, and even poor China and India that experienced a "take off' that dramatically diverges from the median trajectory observed in the global data.

Second, we ask whether the success of several of these countries was due partly to their deviation from the standard path, suggesting that developing countries need to greatly upgrade their R&D efforts, or were these innovation "over-achievers" engaged in wasteful spending? To approach these questions we follow an emerging literature that estimates the social rates of return to R & D. Virtually all studies have used U.S. industry and firm level data and found extremely high social rates of return ranging from 71% (Griliches and Lichtenberg 1984) to over 100% (Terleckyj 1980 and Scherer 1982). Only three studies to date use cross country data, thereby presumably capturing intra-country spillovers. Coe and Helpman (1995) estimate rates of return to R&D of 123% for the G7 and 85% for the remaining 15 OECD countries; van Pottelsberghe de la Potterie and Lichtenberg (2001) find returns of 68% in the G7 and 15% for a subset of the remaining OECD countries. At the long run US cost of capital of 7%, these estimates imply that the optimal levels of R&D should be multiples of their present levels.

To date, the literature relative to developing countries is extremely thin. Lichtenberg (1994) works with a cross section of 53 countries and argues that the return to private R&D is seven times larger than to fixed investment. Coe, Helpman and Hoffmaister (1997) and a sub-sequent literature (Keller 2001) estimate the impact of _foreign_ R&D on manufacturing TFP growth in developing countries. These authors argue that because developing countries' own R&D expenditures are so low, they can be ignored. The data employed here suggest that developing-country R&D is not necessarily insignificant relative to the size of their economies, and more importantly, the returns are substantial. In fact, the returns to R&D in developing countries are above those for industrialized countries.

> Our estimation strategy attempts to deal with several issues raised in the existing literature, particularly those employing single cross sections, related to unobserved country heterogeneity and the likely endogeneity of R&D. For instance, Barro and Sala-

3
I-Martin (1995, 352) find the reported<sup>rates of returnto beimplausibly highandspeculate</sup> that they are due to reverse<sup>causality goingfromproductivitygrowthto R&D</sup> expenditures.

A third question naturally emerges<sup>fromtheaforementionedanalyses:Ifthe</sup> returns are so high in poor countries,<sup>why dorichcountriesinvestmoreinR&Dasa</sup> share of GDP? To answer this question<sup>weexplore potentialdeterminantsof R&D across</sup> countries and over time. We find<sup>thatthedepthof domesticcreditmarkets,educational</sup> variables, the extent of protection<sup>offeredto intellectual propertyrights(IPRs),theability</sup> to mobilize government resources,<sup>andthequalityofcomplementaryacademic</sup> institutions influence cross-country<sup>differencesinR&D,andasubsetofthese variables</sup> together completely eliminate the<sup>apparenteffectofthelevel ofdevelopmentonR&D</sup> effort.

A recurring question throughout the<sup>paperiswhetherandhowmuchofthe</sup> patterns observed across countries<sup>can beexplainedby their endowmentsofnatural</sup> resources. Numerous authors (see<sup>forinstance,SachsandWarner2001,Matusyama</sup> 1991) argue that the prospects for<sup>productivity growthareintrinsically lowerin these</sup> sectors than in manufacturing. Lower<sup>potentialfor TFPgrowthcould implylowerrates</sup> of rates of return to R&D and hence<sup>lower investment.</sup> The rest of the paper is organized<sup>asfollows.Section11focusesoninnovation</sup> trajectories during the development<sup>processby discussingthe dataandthe corresponding</sup> results. Section Im discusses basic<sup>conceptsandmethodsusedfor estimatingthe social</sup> rates of return to R&D. Section IV<sup>thenexploresthedeterminantsof R&D.SectionV</sup> summarizes the main findings.

# **II. Innovation Trajectories: R&D**<sup>**Expenditures and Development**</sup>

The data were drawn from a data base<sup>constructedby Ledermanand Saenz(2003)</sup> for a broad cross section of countries<sup>fromthe1960s to the present. Further de'ailon the</sup> construction of the series is available<sup>therebut the coredataonR&Dwasdrawnfrom</sup> 4
> UNESCO, The World Bank, OECD, the Ibero American Science and Technology

> Indicators Network (RICYT)<sup>5</sup> and the Taiwan Statistical Data Book. The definition of R&D in all these surveys is the same and "includes fundamental and applied research, as well as experimental development." 6 The data thus include not only the basic science expected in the more advanced countries, but also investments in the adoption and

> adaptation of existing technologies often thought more germane to developing countries.

Though it would be desirable to study the evolution, rate of return to, and determinants of private R&D, we work with aggregate R&D for several reasons. First, the data sources divide R&D not into private and public R&D, rather they distinguish between productive and non-productive sectors, the latter accounting for roughly 20% of the total.<sup>7</sup> The definition of "productive sector" includes both public and private for profit and not-for profit firms while "non-productive sector" includes R&D financed or undertaken by the executive branch of government. Since the productive sector may well include mining, public utilities or other state owned enterprises, the exercise of analyzing how its R&D evolves and its rate of return relative to that of non-productive sector is less interesting than the public/private sector split.

Second, this division seems to occasionally lead to some critical issues in categorization. For instance, if a public company finances its R&D from retained earnings, this will count as productive sector R&D. If instead that R&D is financed by a transfer from the treasury to the firm, it counts as "non-productive" R&D. For several countries in our sample, there were striking shifts in composition from one year to the next suggesting such sensitivity to accounting practices. In contrast, the total R&D series were reasonably stable. The final consideration is more prosaic: many developing countries tabulate only the aggregate values and as they are the focus of this paper, we want to include as many as possible.

> **_5_** Red Iberoamericana de Indicadores de Ciencia y Tecnologfa

> **6** UNESCO Statistical Yearbook (1980) pg 742. Definitions are common to the OECD, RICYT, World Bank and all are based on the Frascatti manual definition.

> **7** The median for countries with both series is 21%.

_5_
Figure la plots the predicted<sup>and observedlevelsof R&Das ashareofGDP as</sup> function of the log GDP per<sup>capita.Thepredictedvalueisgeneratedfrom a regression of</sup> the log of the ratio of total<sup>R&DexpenditurestoGDPonlogGDPpercapitaandits</sup> squared term. Due to concerns<sup>about theinfluenceof outliers,thismodelwasestimated</sup> as a median regression. The<sup>estimatedcoefficientsarepresentedin thefirstcolumnof</sup> Table 1, and Figure la illustrates<sup>theresultingpositive relationshipbetweenR&Deffort</sup> and log GDP per capita.<sup>Itisclearfromthisevidence thatR&Dexpenditures/GDPrise</sup> with development and that<sup>therateof increasealso riseswith GDP percapita.Though</sup> the apparent curvature is partly<sup>afunctionof thelog transformationof GDP per capita</sup> employed to more clearly<sup>displaythedifferencesamongpoorcountries, theelasticities</sup> estimated in Table 1 eventually<sup>exceedunitysuggestingthatthesecondderivativeis, in</sup> fact increasing. The fixed effects estimates<sup>incolumn2indicatethatthenon-linear</sup> between<sup>R&Dandthelevelof developmentisaphenomenon that</sup> positive relationship occurs within countries, and<sup>it is not an exclusive feature of thecross-country variations.</sup> Figure lb presents a first<sup>cutat lookingat howa fewselect countriesfrom several</sup> regions compare to the<sup>predictedvalue.Whatis immediatelystrikingisthatKorea,</sup> Finland, and Israel show<sup>substantial "takeoffs"relativetothemediantrajectory.Two</sup> Latin American countries,<sup>Argentinaand Mexico, whichhadsimilar levelsof incomeas</sup> Korea and Israel prior to<sup>theirtakeoff hoveronor belowthepredictedvaluefor their</sup> level of development. Both<sup>China andIndiaappear to be followingmorein thefootsteps</sup> of the "take off" countries<sup>than the LatinAmericans.</sup> Figures 2a-d present the residuals<sup>from a more generalandflexible specification</sup> that includes log GDP, log<sup>GDPsquared,loglaborforce,and log laborforcesquared,</sup> and year dummies as explanatory<sup>variables.Thisallowsforindependenteffectsrelated</sup> to the size of the economy<sup>andsizeof thelaborforceratherthan percapitaincomeor</sup> development per se. In this<sup>case,the predictedvalueiscaptured bythehorizontalaxis</sup> and we observe the evolution<sup>of countryR&Deffortacrosstimeasopposedtoacross</sup> income levels. The selection of countries<sup>reflectstheavailabilityof datainthecaseof</sup> the developing countries<sup>andanattempt to presentabroadcrosssectionoftypesof</sup> countries. For the developing<sup>world, we focuson Latin Americaand Asia.Africa'sdata</sup> 6
> is of generally poor quality and the time series from the emerging Eastern European are still relatively short.

The results broadly support the conclusions from figures 1. Both Korea and Taiwan show impressive rises to roughly 100 percent above median levels in the early 1980s that continue to this day. Both India's and China's residuals suggest long standing above median investment that seems to have declined somewhat in the 1990s. This appears due not so much to a decline in the absolute amount of R&D spending, but rather some lag in keeping up with the relatively high growth rates of this period. This highlights, in particular, the achievements of Korea and Taiwan in maintaining an increasing level of R&D in periods where GDP was growing at rates close to 10% per year. The two little tigers, Thailand and Indonesia, show very different trajectories suggesting that they are, in fact, not following closely in the footsteps of the successful Asian economies in the innovation dimension. The decline starts long before the crisis of the late 1990s and most of the trend is due more to a stagnation of R&D spending in the face of rising GDP than absolute falls.

The new benchmarking presents a more pessimistic view of the evolution of Latin America's R&D effort. Argentina and Chile are only very rarely above the median with Chile the most consistent performer near the median. In the cases of Argentina, and to a lesser extent, Venezuela and Chile, R&D effort has declined secularly relative to the median. The rest of the region has muddled along at roughly 50% of the median for much of the period, although Latin American countries seem to have approached the conditional median in the late 1990s. Given the sharp falls in incomes in most of these countries during the lost decade of the 1980s, the stagnation or decline relative to the median represents absolute declines in total innovation effort.

Two questions immediately come to mind. The first is whether the low R&D spending is to some degree a result of specialization in natural resource intensive products. It is striking that Indonesia and Thailand as well as Latin America are natural resource abundant countries as reflected in high net exports of resource intensive commodities as defined in Leamer (1984). However, Figures 2c casts some doubts about 7
such a link between R&D and natural resources.<sup>Whilethe overallgoalis topresent the</sup> trajectories of more advanced countries, we<sup>dividethe sampleintothoseadvanced</sup> countries that are ranked as abundant in factors used<sup>intensivelyinmanufacturedexports</sup> (2c) and those abundant in natural resources<sup>(2d).What is strikingisthatFinland,</sup> Sweden and the Netherlands, all resource abundant<sup>areconsistentover performerswhile</sup> Canada, Australia and Ireland are pretty consistently<sup>atthemedian.</sup> In, fact, Trefler (1999) argues that Canada's very average<sup>performanceispartially responsiblefor</sup> Canada's lack luster performance relative<sup>to the U.S.</sup> The more manufacturing oriented countries, also<sup>showaverydiversepicture.</sup> Israel and Switzerland have shown consistently<sup>abovemedianperformance withIsrael's</sup> trajectory since 1975 especially striking (see Trajtenberg<sup>2001foradiscussion).Japan</sup> and the U.S. pretty much defined the trend although<sup>they slippedbelowthe medianin the</sup> 1990s. It is worth mentioning that at higher levels of<sup>incomethesample becomesvery</sup> thin so the benchmarking becomes somewhat<sup>lessinformative.</sup> The U.S. and Japan invest very high shares of GDP in R&D but<sup>Switzerlandinvests moresothefirsttwo</sup> appear "below median." Spain has slowly emerged<sup>to joinItalyatmore orlessLatin</sup> American levels of underperformance.

These findings naturally lead to the second<sup>questionofwhethertheunusually</sup> high levels of R&D in some countries, and particularly<sup>thedramatictakeoffsof Finland,</sup> Israel, Korea, and Taiwan were justifiable investments<sup>orwhetherinsomesensethey</sup> may reflect a new type of technological<sup>whiteelephant.</sup> All are very successful countries, but to what degree do they owe this to<sup>theireffortsinR&D?To answer this</sup> question we turn to an analysis of the social rates<sup>ofreturntoR&Dinthefollowing</sup> section.

8
# 1[l11. Rates of Return to R&D Expenditures

## _A. The standardframework_

A simple production function (see Jones and Williams 1998) _Y = KA LA6 S6_ where Y is the level of output, K the level of physical capital, L the labor stock and S the stock of accumulated R&D, can be rewritten as by using the fact that Here rx is the rate of return on factor X, x is the share of investment in X over Y, and _/3x_ is the output elasticity of factor X . If we remove the influence of physical factors to get **TFP** then the social rate of return to R & D is

## _r, = AlnTFP/s_

where s is the share of R&D spending in income. The optimal level of R&D expenditure occurs where rs= r, the real interest rate. So, the ratio of the optimal level of R&D investment to actual along a balanced growth path can be expressed as the ratio of the social rate of return to R&D over the real interest rate or opportunity cost: 9
_s r_ _s r_ Jones and Williams argue that for a very conservative<sup>estimateof 28%returnto R&Din</sup> the US, a long run 7% rate of return on<sup>the stock marketoverthelast century suggests</sup> that the U.S. should be investing perhaps 4<sup>times the presentR&D levelobservedin this</sup> country, which averaged approximately 2.6%<sup>of GDP during1995-2000.</sup>

# _B. Estimation of the rates of return_

We begin with a basic specification that can<sup>nest much of the existingwork on the</sup> empirics of economic growth: Where yi, dot is the log difference of per<sup>capitaGDP of countryi inperiodt,y,,tlis log</sup> income per capita at the beginning of<sup>theperiod,X thematrixof conditioning variables,</sup> in this case the growth of labor,<sup>**s,t**</sup> represents<sup>investmentsin both physicaland innovative</sup> capital expressed as a share of income.<sup>piis anindividualcountryfixedeffect,Rtisa</sup> sample-wide time effect, and **Eit** is a country<sup>and time specificeffect.</sup> One important type of omitted variable bias<sup>mightbeinduced bythecorrelation</sup> of unobserved country-specific factors<sup>andthevariablesofinterest;E(I.,,</sup><sup>**s,t)**maybe</sup> large. Casselli, Esquivel and Lefort (1996),<sup>for instance,pointedoutthatthedifference</sup> with respect to the highest level of income<sup>inthe sampleof countries(i.e.,thelevelto</sup> which she other countries are converging)<sup>acts as aproxy of the country-specific effectin</sup> cross sectional regressions, and thus the<sup>resultingestimatesare inconsistent.</sup> Panel data offers the only real solution<sup>totheendogeneityproblem throughthe</sup> use of lagged values as instruments for<sup>endogenousvariables. Theissue of unobserved</sup> country specific effects can also be<sup>addressedalthoughthestandardfixedorvariable</sup> 10
effects estimators are not consistent in the present context that implicitly includes a lagged dependent variable -- the initial level of GDP per capita. The assumption of a lack of correlation between ,4 and the explanatory variables required for variable effects estimators is not defensible in this context since both yt dot _and_ Yt-i are a function of **ji,.** On the other hand, OLS is clearly inconsistent and FGLS is also if the errors show either heteroskedasticity or serial correlation (Sevestre and Trognon 1996). Further, the usual elimination of **i,** by subtracting the country mean induces a negative correlation between the transformed error and the lagged dependent variables of order 1/T, which, in short panels such as those used here remains substantial. If at least one of the explanatory variables is truly exogenous, Balestra and Nerlove (1996) show that its lags can be used as instruments and will yield consistent estimates. However, in the present case, it is difficult to assume that any of our variables are strictly exogenous.

> Following Anderson and Hsiao (1982), Arellano and Bond (1991) and Caselli et.

> al. (1996) in the growth literature, we therefore difference the data to eliminate i, thus yielding:

Any unobserved country fixed effects disappear in the differenced errors. However, unless the idiosyncratic error followed a random walk, this differencing necessarily gives the transformed error a moving-average, MA(n), structure that is correlated with the differenced lagged dependent variable. This can be overcome by using instruments dated t-n and earlier. Arellano and Bond (1991) employ lagged levels as a proxy for differences in a Generalized Method of Moments (GMM) context. However, in growth regressions where the explanatory variables (e.g. schooling, natural resource endowments) show little variation over time, levels are often poor instruments. For this reason, Levine, Loayza, and Beck (2000) in their examination. of the impact of financial variables on growth follow Blundell and Bond (1998) and Arellano and Bover (1995) in employing a system estimator that rescues some of the cross-sectional variance that is lost in the differences GMM estimator by estimating a system of equations that also 11
includes equation (1) in levels,<sup>butwiththelagged differencesoftheendogenous</sup> variables as instruments. Bond<sup>etal.(2001)showthatthe"weakinstruments"problem</sup> can be severe in cross-country<sup>growthregressionswith panel data.Thereforewefollow</sup> them, as well as Levine et al.<sup>(2000)inapplyingtheGMM</sup><sup>_system_estimatortoour</sup> growth models.

We follow Griliches (1995) in estimating<sup>the growthregressionasin equation (2),</sup> rather than estimating the TFP<sup>residual firstandthenusingthat residual asthe dependent</sup> variable of R&D. The single regression<sup>approachissuperiorinthis case,becausewe are</sup> interested in the returns to R&D<sup>investmentrelativetophysicalcapitalinvestmentsand</sup> thus we want to retrieve mutually<sup>consistentestimates of the returns for bothvariables.</sup>

# _C. Results_

The core data combines the R&D<sup>datawiththat of SummersandHeston(1991)</sup> panel updated to 2000 and the<sup>Leamermeasureof resource endowments. We estimatethe</sup> corresponding growth regressions<sup>usingpaneldataof five-yearaveragesbetween1975-</sup> 2000. Table 2 presents estimated<sup>returnsto R&Dforthe panelofcountriesthathad</sup> sufficient consecutive observations<sup>(atleastthree)requiredfortheGMM system</sup> estimator. All the regressions pass<sup>theSargantestfor thevalidityof theinstrumentsand</sup> there is no evidence that they<sup>sufferfrom residual secondorderserialcorrelation.8</sup> Column 1 indicates that, for the<sup>largestpossiblesample,returns to R&Dasa whole are</sup> around 78%. This estimate falls<sup>inthemiddleof those previouslycitedestimatesforthe</sup> U.S. and the OECD estimates from<sup>Coe and Helpman(1995).</sup> To see if this aggregate number<sup>ishiding variation acrossdifferentlevelsof</sup> development either across countries<sup>orwithincountries,weinteracttheR&Dtermand</sup> the physical investment term with<sup>per capita GDP.The negative R&Dinteractionterm in</sup> column 2 suggests a _decreasing_<sup>returnto R&D withdevelopment.Thisisconsistentwith</sup> the intuition of numerous conditional<sup>convergenceregressionsintheBarro(1991)</sup>

> 8 The null of the Sargan test is that<sup>thereis no correlation betweentheerrors andtheinstruments.Thusa</sup> high p-value indicates that the<sup>Instruments arenot correlatedwiththe errors.</sup>

12
tradition: It is likely that a dollar's worth of R&D buys greater increases in productivity for countries far from the technological frontier than for innovating countries who must invent the new technologies that push the frontier forward. Figure 3 plots the predicted values of both R&D and physical investment. The return in the average OECD country is somewhere between 20-40%. For medium income levels, such as Mexico and-Chile, the average return is around 60% and for relatively poor countries, such as Nicaragua, the average return is closer to 100%.

> Figure 3 also plots the estimated return to physical capital, which column 2 also suggests is decreasing in level of development. As a first conservative approximation,

> we treat this as the cost of investment resources and hence the ratio of the two returns gives us the ratio of optimal R&D investment to actual. For the U.S., the gap of 2.25 is somewhat more moderate than that offered by Jones and Williams (1998). The return to R&D rises for poorer countries, but given that return to physical capital also rises, their ratio also remains under 2.5. Since the high rate of return to physical investment in developing countries may reflect the risk or other factors that would drive a wedge

> between it and the true cost of borrowing, this calculation may be excessively

> conservative. If instead, we go to the other extreme of assuming free access to

> international capital markets and that the opportunity cost of capital is the **20th** century's return on the U.S. stock market of roughly 7%, as suggested by Jones and Williams, the

> gap for countries of Mexico's or Chile's development level would rise closer to 8. This would suggest that the deviations from the central tendency exhibited by Israel, Finland, Korea and Taiwan were fully justifiable by the rates of return to R&D.

The remaining columns of Table 2 introduce other variables that may explain the returns, but whose availability reduces the sample by 10 countries and, for several countries, the observations available for use as instruments from the 1960s thus shortening the overall period of estimation as well. Column 3 repeats the specification in column 1 with the reduced sample, and we immediately see that the returns to R&D have 13
now risen to 133% and<sup>thoseofphysicalcapital to18.9%.9Thedramaticriseofthe</sup> former is likely to be an<sup>artifactofthecountries thatweredropped, whichinclude</sup> Colombia, Guatemala,<sup>Iran,Jamaica,Jordan,Malta,Mauritius, TogoandZambia,which</sup> could be under-performers<sup>relativeto the averagereturns to R&Damong poor countries.</sup> Nonetheless, the returns<sup>areclearlysensitivetothesampleofcountriesandthus the</sup> results should be interpreted<sup>withsome caution.</sup> Column 4 adds both the<sup>tertiaryeducationenrollmentrateasapossiblefourth</sup> production factor and the<sup>Leamernet exportsofnatural-resource intensiveexportsto</sup> control for postulated impacts<sup>ofnaturalresourcesongrowththroughtheinvestment,</sup> education, or R&D channels.<sup>Highereducation does appear tohaveapositiverateof</sup> return and natural resource<sup>abundance appearspositivelycorrelated withgrowth,</sup> consistent with Lederman<sup>andMaloney(2003).The coefficientson theother factors do</sup> not change dramatically<sup>as aresultalthoughthe returntoR&Dnowfallsto102%.</sup> Columns 5 and 6 interact<sup>the physicalcapitalandR&DinvestmenttermswithGDPper</sup> capita and, in addition, the<sup>R&Dandresourceabundance termsto see whetherresource</sup> abundance in fact hampers<sup>innovationbasedgrowth.</sup> Column 7 confirms the<sup>decliningreturnstoR&Dand physicalcapitalwith</sup> development. It also suggests<sup>thatR&Dandnaturalresourcesare strongcomplements;</sup> the returns to R&D rise<sup>withnaturalresourceexportsandvice-versa.Thisis consistent</sup> with Martin and Mitra's<sup>(2001)finding that agriculture has experiencedamuch faster</sup> rate of TFP growth than manufacturing<sup>in most developedanddevelopingcountries.It is</sup> also consistent with<sup>theargumentthatthebetterperformanceof,forinstance,</sup> Scandanavia or Australia<sup>-in their exploitationofnaturalresources comparedtoLatin</sup> America can be explained<sup>bytheir muchhigherR&D effortandother innovation-related</sup> policies (Maloney 2002).<sup>Since theSargantests rejectsthe adequacyoftheinstrument</sup> set, column 6 represents<sup>is themostcompletewellspecifiedregression achievable,</sup> although it drops the physical<sup>capital interactiveterm. The previous resultshold.</sup>

> 9 In fact, putting in education<sup>alone decreasesthe sampleby5countriesbut somewhat counterintuitively</sup> pushes the return to R&D<sup>to98%.This is likely tobe due to the same selectionissue discussedbelow.</sup>

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The previous findings that poor countries invest less in R&D than rich countries and that the returns to R&D are higher in poor countries beg the question of why poor countries invest less in R&D. The following section addresses this question.

> IV. Determinants of R&D: Why Do Rich Countries Spend More than Poor Countries?

# _A. Related literature and data_

There are very few studies of the determinants of R&D across countries. Two such studies (Varsakelis 2001; Bebczuk 2002) suffer from small samples and, as result, inconsistent estimates due to inability to deal with country-specific effects and endogeneity of the explanatory variables. Here we again apply the GMM system estimator to our larger sample.

We begin by analogy to the investment literature (see Serven 2003, for example) assuming equilibrium where the marginal product equals the cost of borrowing, but then ask what factors may impede this equalization and hence explain why, given the high estimated rates of return, we do not observe more R&D investment.

We first include a proxy for the long term real cost of borrowing. As a first approximation, we employ the nominal 30-90 day lending rate deflated by the CPI. Though this has the most international coverage in the IMF _International Financial Statistics,_ it is still not complete. Further we eliminate countries showing negative real interest rates or values above 40% which we assume are unsustainable long run levels, and capturing stabilization problems, as was the case in Argentina and Brazil in the 1980s. Together, these imply a reduction in the sample to 30 countries. As a second alternative we include the actual gross fixed investment rate which we presume reflects the opportunity cost of investment as well as other factors pertaining to the investment climate. A disadvantage is that the impact of other variables determining R&D investment must be interpreted as effects beyond what they may have on physical 15
investment. Finally, we follow David et al. (2000) arguments in including a measure of credit market depth measured as the ratio of credit to the private sector relative to GDP to proxy for the availability of credit at the reported interest rate.

A second set of variables seeks to capture risk associated with long term investments. Following Serven (2003) we include the variance of GDP which he found correlated with physical investment.

We also include a measure of intellectual property rights that would also affect the expected quasi rents derived from innovation. Although the impact of IPR is theoretically ambiguous (Horstmann et. al 1985), Arora, Ceccagnoli and Cohen (2003) using US manufacturing survey data find that patent protection stimulates R&D across almost all industries. For this purpose we use the IPR index constructed by Park (2002).

To control for the fact that we are using a series of total R&D expenditures, which includes private and public financing of R&D, we include a measure of overall government spending over GDP as a measure of the government's capacity to mobilize resources. As possible further constraints on investment, we include measures of the availability of complementary innovation-related institutions that may also put binding constraints on new R&D projects. We include the subjective indicators from the _Global Competitiveness Report_ (GCR) published by the World Economic Forum on the quality of research institutions (universities, public research centers, etc) and the extent to which these collaborate effectively with the private sector. These considerations may also constrain the number of national innovation projects. Due to the limited time coverage of these two variables, we use the average of the available observations from the late 1990s and assume that these change little over time.

Finally, we include the Learner indicator of natural resource abundance to see if there is evidence of special barriers to R&D investment in natural resource abundant countries that may somehow explain the especially high rates of return found there.

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In all specifications we include as control variables the lagged dependent variable, GDP growth to capture cyclical or accelerator effects, and the log of GDP/capita which we know from section II is positively associated with the R&D effort. Finding correlates that eliminate this last effect and explain why richer countries invest more is a key goal of the exercise.

# _B. Results_

Tables 3a presents the results using the real interest rate and reduced sample. Table 3b presents those with the fixed investment proxy. Both sets of models were estimated using the GMM system estimator. We begin with the core specification including the proxy for the opportunity costs of investment. Since the samples are of modest size, we first individual add variables to the core specification test for their significance and their impact on the level of development variable. Finally, the last columns attempt, to the degree the samples permit, the inclusion of several of these variables together. The increased number of instruments leads to a deterioration in the power of the Sargan tests, particularly in the small sample. In all specifications, with the exception of those specifically mentioned, the Sargan and serial correlation tests are satisfactory.

In both specifications, column 1 shows similar estimates of the lagged dependent variable and level of development which, in the former case, are reasonably robust across specifications. The control for cyclical influences, the GDP growth variable, is consistently positive and significant in the larger sample although less consistently in the smaller one.

The real interest rate has the expected negative sign in all but one specification and is often although not uniformly significant. This intermittence is commonly found in the investment literature (see Serven 2003) but the overall impression is that higher borrowing costs do lead to lower investment in R&D. The investment proxy offers less intuitive results, entering negatively in virtually all but one specification, and is often 17
significant. This may suggest competition among the two types of investment for savings as opposed to a complementary fixed resources. In fact, simple scatterplots suggest that while R&D/GDP rises with development, I/GDP follows an inverted U, perhaps reflecting the diminishing role of physical capital in economies more dependent on innovation for their growth. As we will see, however, the variable is still picking up key elements of the investment environment.

Private credit in both specifications enters with the predicted positive sign indicating that deeper capital markets facilitate R&D investments. Among the measures of risk, the standard deviation of growth enters with the expected negative sign in the interest rate specification and makes the interest rate coefficient significant. In. the investment specification, the risk variable has the expected positive sign but it is not significant. However, this appears to be because the investment rate is already capturing it. Dropping investment from the specification makes macro-economic risk enter significantly. In both specifications, the IPR protection index has the expected positive and very significant coefficient. In the larger sample, it causes a reduction in the magnitude of the level of development proxy by roughly half although no such effect is found in the smaller sample.

The same is true for the proxy for the government's ability to mobilize resources. Of interest is also that the physical investment proxy now becomes insignificant but with the expected positive sign. The negative correlation of the two may reflect a reverse causality from that postulated: a greater government effort in R&D has the impact of crowding out physical investment. The results from the smaller sample tell perhaps a different but reasonable story. Once we control for the government contribution to R&D, the private component is more sensitive to interest rate movements than the aggregate value. Also worth noting is that a larger ability to mobilize resources appears to account for roughly half of the impact of the GDP/capita variable on R&D effort in the large sample and a quarter in the small.

Both measures of complementary research capacity enter strongly significantly and with predicted sign in both samples. Of equal interest is that the introduction of the 18
> quality of research institutions variable eliminates the influence and significance of the level of development variable and reduces its magnitude and significance in the large sample. What is suggested is that, of any single variable, the dearth of quality research institutions is the most influential in explaining why R&D projects with very high expected returns go unexploited in the developing world. Finally, a negative and significant relationship emerges between Leamer's measure of resource abundance and R&D spending in both specifications.

Combining these variables in one specification degrades the Sargan test in both specifications and our treatment here is cautious. In the large sample, the IP, private credit, and government consumption variables maintain their significant and positive effects. We lose three countries introducing the Leamer measure, and the Sargan tests become unacceptable (not shown) but the previous results are preserved and the sign on natural resources is again negative. Though losing its significance in the specification in column 9, the quality of research institutions variable also becomes significant and positive again in the reduced sample with the Leamer measure, but again with an excessively high p-value for the Sargan test. More importantly, the level of development is not significant in any of these comprehensive specifications.

The small sample estimates using the real interest rate proxy are more fragile but we report even the specifications with exaggerated p-values of Sargan tests because they are suggestive and broadly consistent with those just discussed. Column 9 suggests that again, the IP index, government spending and the quality of research institutions measure remain significant and eliminate the effect of the level of development. In the alternative specification in column 10, dropping the IP index and including the private credit depth variable causes the latter to appear at the 12% significance level, and the former to become significant at the 10% level.

Hence the evidence suggests that the level of development is positively correlated with R&D effort mainly because rich countries tend to have better IP protection, deeper credit markets, higher government capacity to mobilize public R&D expenditures, and in all likelihood, the better quality of the research institutions.

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The negative sign on natural resource<sup>abundance meritsfurtherattention,</sup> especially since the returns to R&D rise with net<sup>exportsof naturalresources</sup> The most straightforward explanation may simply<sup>bedefinitional.R&Ddata"[do]not include</sup> research on the soils for agricultural purposes,<sup>foroceanographyservingthefishing</sup> industry, concerning the economic exploitation<sup>ofsourcesofrawmaterials, fueland</sup> energy, nor concerning the use of satellite techniques<sup>for communicationapplications"</sup> (UNESCO 1980 p 743). It is not obvious<sup>whysoilresearchoroilexploration,which</sup> have very clear implications for productivity,<sup>shouldbeexcluded,butitislikely that</sup> countries specializing in natural resources will<sup>haveuncountedresearchexpenditures.</sup> The high estimated returns to R&D in natural<sup>resourcerich countriesmayalsobe</sup> partially due to upward bias to the degree<sup>thatresearcheffortin theseeconomiesis</sup> under-estimated by the available R&D data.

Alternatively, perhaps the low R&D investment<sup>inresourceabundantcountries</sup> occurs because the rents or market power associated<sup>withnatural resourceexploitation</sup> allow these economies to perform relatively<sup>welloverthemediumtermwithoutmuch</sup> innovation effort (Landes 1998): though the Netherlands, Finland and Sweden<sup>have</sup> chosen a high innovation path, Latin America,<sup>Indonesiaand Thailandareperhapsmore</sup> representative of resource rich economies<sup>that have followeda lowinnovationpath.This</sup> is consistent with Howitt and Mayer's (2002)<sup>modelof convergenceclubsand Maloney's</sup> (2002) application of it to explain Latin America's<sup>disappointingnaturalresource</sup> performance.

# V. **Summary of Findings**

This paper traced the evolution of innovation<sup>indicators alongthedevelopment</sup> process using international data on R&D expenditures.<sup>Thefirstpartofthepaper</sup> illustrated how R&D expenditures vary with the<sup>levelofdevelopment.Asexpected,</sup> R&D effort rises with development but at<sup>an increasingrate.In turn, the paper examined</sup> how different groups of countries (Latin<sup>America,East Asia,andnatural-resource-rich</sup> 20
> countries from the OECD) fare relative to what is expected from their GDP and labor

> force. We found that Latin America under-performs relative to the predictions, but the other country groups significantly over-perform.

Does this mean that these countries over invest in R&D? The second part of the paper estimated social rates of return to R&D and showed that the returns fall with development. The estimated returns for all countries are high, but our results for the U.S. and the OECD are in the lower bound of existing estimates. Future research could estimate the extent to which R&D-driven patenting activity explains the large returns. Finally, the paper attempted to explain why rich countries invest more in R&D than poor countries, given that the rates of return are higher in the latter. We conclude that financial depth, protection of intellectual property rights, ability to mobilize government resources, and research institution quality are the main reasons why R&D effort rises with the level of development. Finally, the returns to R&D rise net exports of natural resources, resource abundant countries in fact grow faster on average, and the application of R&D is critical to reaping those benefits.

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