Innovation, Tangible and Intangible Investments and the Value of Spanish Firms

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Innovation, Tangible and Intangible

Investments and the Value of Spanish Firms*

by Aitor Lacuesta**, Omar Licandro***,

Teresa Molina**** and Luis A. Puch*****

Documento de Trabajo 2009-19

June 2009

* We thank Juan Ramón García and Massimiliano Marinucci for excellent research assistance through different stages of this project. We also thank Jorge Durán, Elena Huergo, Juan Francisco Jimeno, Pedro Mendi, Teodosio Pérez, Dirk Pilat, José Antonio Moreno and Adelaida Sacristán for very helpful comments, as well as seminar audiences at Granada SAE 2007, Toulouse Knowledge Conference, and Barcelona Zvi Griliches Summer School. Part of this research was done while Puch was visiting the European University Institute under the Fernand Braudel fellowship programme which is gratefully acknowledged. Financial support from Fundación COTEC and the Fundación Focus-Abengoa, as well as the Dirección General de Investigación, project SEJ2007-65552, are gratefully acknowledged.

** Bank of Spain. *** European University Institute. **** World Bank and FEDEA. ***** Universidad Complutense and FEDEA. Los Documentos de Trabajo se distribuyen gratuitamente a las Universidades e Instituciones de Investigación que lo solicitan. No obstante están disponibles en texto completo a través de Internet: http://www.fedea.es. These Working Paper are distributed free of charge to University Department and other Research Centres. They are also available through Internet: http://www.fedea.es. ISSN:1696-750X

Innovation, Tangible and IntangibleInvestments and the Value of Spanish Firms∗

Aitor Lacuestaa, Omar Licandrob, Teresa Molinac and Luis A. Puchd†

aBank of Spain

bEuropean University Institute

cWorld Bank and FEDEA

dUniversidad Complutense and FEDEA

April 2009

Abstract

Why is R&D spending so low in Spanish firms? One possible answer may liein a small contribution of innovative investments to value creation at the firm level.When pulling together complementary sources of spending data and related evidenceto measure these investments, we observe that R&D is low for international standards,but overall intangible investment seems adequate. Data from the Central de Balancesare then used to assess the effect of R&D and other innovative investments on thevalue of Spanish firms. The results suggest that intangible investments have a positiveimpact on market values which is more substantial for innovative sectors, and this isalso the case for R&D capital. Such a positive impact is influenced by the size of thefirm and its presence in the stock market. In fact, an alternative explanation to lowR&D intensity could be found in the small fraction of firms publicly traded in thestock market in Spain, as far as equity holders tend to value intangible assets morethan bond holders. Consequently, promoting a more active role of market valuationsas a guide for innovative investment might be a promising policy.

Keywords: Tangible Investment, Intangible Investment, Market ValueJEL Classification: E22, C33, L60

∗We thank Juan Ramon Garcıa and Massimiliano Marinucci for excellent research assistance throughdifferent stages of this project. We also thank Jorge Duran, Elena Huergo, Juan Francisco Jimeno, PedroMendi, Teodosio Perez, Dirk Pilat, Jose Antonio Moreno and Adelaida Sacristan for very helpful comments,as well as seminar audiences at Granada SAE 2007, Toulouse Knowledge Conference, and Barcelona ZviGriliches Summer School. Part of this research was done while Puch was visiting the European UniversityInstitute under the Fernand Braudel fellowship programme which is gratefully acknowledged. Financialsupport from Fundacion COTEC and the Fundacion Focus-Abengoa, as well as the Direccion General deInvestigacion, project SEJ2007-65552, are gratefully acknowledged.†Corresponding Author: Luis A. Puch, FEDEA, Jorge Juan 46, 28001 Madrid, Spain; E-mail:

[email protected]

1 Introduction

Innovation is a complex process involving both creation and adoption. These activities re-

quire investments in human and physical capital, together with some other forms of intangible

assets. Indeed, not all investments are innovative. However, those activities accumulating

in knowledge allow for the progress of Total Factor Productivity (TFP) and the creation of

value in the firm.

The question of how investment in different types of capital brings about innovation,

and how this process affects the performance of the economy is of considerable importance.

A large academic literature has built upon the sources-of-growth framework developed by

Solow (1957, 1960) and others, in the 1950s and 1960s, for sorting out the factors that drive

Total Factor Productivity (TFP) growth. Advances in this framework have lead to questions

related to the emphasis on quality change in the measurement of prices, or to the effects of

capitalized intangible investments. As for the latter, from the theoretical side, the intangible

capital extension of neoclassical growth theory has been used to measure the amount of

intangible assets in the US economy (McGrattan and Prescott (2006)). Alternatively, from

the empirical side, a number of recent papers have measured business spending on intangible

capital (Corrado, Hulten and Sichel (2005)) and quantified its role relative to the various

types of produced capital, by estimating production functions (see Doraszelskiy and Jau-

mandreu (2007), and the references therein) and market value equations (Brynjolfsson and

Hitt (2002) for the US, or Hall and Oriani (2006) for the US and some EU economies).1

In this paper, we retain the empirical approach to measure tangible and intangible assets

and to quantify its effect on the market value of Spanish firms, notably the impact of R&D

activity. We proceed in two steps. First, we build a measure of tangible and intangible

investment in physical and human capital in line with Corrado et al. (2005). To this purpose,

we report measured National Accounts investment data and we pull together complementary

pieces of spending data on intangible assets (Encuesta de Innovacion Tecnologica (EIT),

1An OECD panel aims at improving estimates of the scale of investment in intangible assets at the nationallevel for selected countries (Finland, Japan, Netherlands, United Kingdom and United States) based uponsuch an intangibles measurement approach. As for production function estimation, as early as Griliches(1979) proposed to augment the production function with the stock of knowledge capital, as proxied by afirm’s accumulated R&D expenditures.

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Panel de Innovacion Tecnologica (PITEC), and other related evidence). This measure gives

insight on the different investment intensities and its evolution at the aggregate level. Second,

we estimate the impact of measured intangible investments relative to tangible investments

and proxies for unmeasured intangible assets from the firm-level data collected in the Central

de Balances of the Bank of Spain following the market value approach. The fact that the

Central de Balances (Firm’s Balance Sheets) database is an incomplete census of the non-

financial business sector in Spain precludes its use to build a measure of aggregate spending

on tangible and intangible assets. Therefore, we draw inference from the micro estimates to

the patterns of aggregate investment to ask whether the low R&D intensity observed in the

Spanish economy has to do with a small contribution of R&D capital to the value of the

firm.

Several authors have explored the economic consequences of different intensities in in-

tangible capital investment relative to tangible investment either among firms or countries.

Notably, the question of how large is the stock of intangible capital is crucial to properly

measure productivity during times of changing investment, as it has been the case of the

last two decades. Abowd et al. (2005) and Black and Lynch (2005) discuss how intangible

assets, and notably R&D capital, contribute to explaining differences in productivity among

firms. At the aggregate level Parente and Prescott (2000) show that intangible capital is

needed to account for differences in per capita income among countries. Also, intangible

capital has been considered of much interest to judge whether the stock market is correctly

valued (McGrattan and Prescott (2005)). Another related approach has used the market

value of the firm as an indicator of the economic results from investing in knowledge capital

[cf. B. Hall (1993)] or to infer the product of capital in relation to the quantity of intangible

capital. For instance, R. E. Hall (2001) discusses how increases in the ratio of stock-market

value to capital are associated with high values of the product of capital. Unmeasured in-

tangible capital is a residual after subtracting the capitalized value of measured tangible and

intangible assets from the total value of corporations.

As these authors we relate tangible and intangible investment to TFP growth or market

values. Different from them we stress on a broad description of the input of innovative activ-

ities related to the creation and adoption processes. In particular, we ask whether innovative

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investments have a stronger impact on market values, and the database we use is unique for

this purpose. Behind the argument is the idea that technical progress is embodied in new

equipment or new vintages of human capital. Corrado et al. (2005) label expenditures on

intangibles as knowledge capital in the sense that it is well represented by intangible capital

accumulation. This knowledge often takes the particular form of innovative investments in

R&D. Whether TFP growth or value creation is a function of “fundamental” research or to-

tal business spending in intangibles is mostly a measurement issue, possibly sector-specific.2

Further, R&D is technology, not capital. Consequently, it is difficult to interpret what does

a low investment intensity or a small stock of R&D capital mean.3

To provide further insight on the size and extent of R&D investment, aggregate data

from various surveys are used to measure R&D expenditures as well as tangible and intan-

gible investments in physical and human capital. We find that R&D investment is low for

international standards, whereas overall intangible investment turns out to be of the right

magnitude. While it is true that R&D is exhibiting the higher growth rates in recent years

among the different categories of intangible investments, this enhanced activity is not enough

to compensate the initial unbalance. The question is then whether the Spanish data of the

financial statements of firms in the Central de Balances can help to interpret this quantity

anomaly. The finding is that the market value of non-financial firms in Spain turns out to

be meaningfully related to their knowledge assets, and in particular with R&D capital. This

link is stronger the more technological is the sector the firm belongs to. Therefore, a low

R&D intensity in the Spanish economy does not come from a small return of this form of

investment. However, an issue to be addressed is that the impact of R&D activities exhibit

a high correlation with size and the firm’s presence in the stock market. The small number

of publicly traded firms in our sample precludes a deeper look into the role exerted by pub-

lic stock markets. Nevertheless, the fact that intangible assets are more valued by equity

holders than by bond holders, and its implications for R&D intensity, is being explored in

Lacuesta et al. (2008).

The paper is organized as follows. Section 2 examines the aggregate investment position

2In Licandro and Puch (2008) an empirical implementation of these methods to the energy sector isdiscussed, with application to the subsector of renewable energies as a case study.

3Compared to capital, technology or knowledge capital embeds an invention cost (once per technology)and an adoption cost (once per user), beyond these there are only user costs [cf. Jovanovic (1997)].

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of the Spanish economy in connection with innovative activities, and provides estimates for

the various sources of tangible and intangible capital. Section 3 proceeds with an econometric

evaluation of the creation of value at the firm level of alternative investments. Each section

discusses its corresponding theoretical background. Section 4 draws inference from the micro

evidence obtained to asses innovation in the Spanish economy from its aggregate investment

position. This section includes as well some concluding remarks.

2 The Inputs for Innovation in the Spanish Economy

2.1 Innovation and Investment

Innovation is a complex phenomenon. To characterize innovation we build upon the op-

erative definitions reported in official statistics. For instance, the Community Innovation

Survey (CIS, EUROSTAT) reports data on product innovation, process innovation and or-

ganizational innovation, as well as more recently on marketing innovation. The following

EU-wide definitions of the Oslo Manual can be used. Thus, a product innovation is the mar-

ket introduction of a new or a significantly improved good or service. A process innovation

is the implementation of a new or a significantly improved production process, distribution

method or support activity for the firm’s good or services. In both cases it must be new to

the firm not to the sector or market, and it does not matter if it was originally developed by

the firm or by other firms, that is created or adopted. On the other hand, an organizational

innovation is the implementation of new or significant changes in firm structure or manage-

ment methods intended to improve firm’s use of knowledge or the efficiency of work flows.

A marketing innovation is the implementation of a new or a significantly improved designs

or sales methods. Again, in both cases it must represent an increase in the quality or appeal

of firm’s goods and services.

Clearly, all these environments for innovation can be related to different forms of tangible

and intangible investments. Indeed, not all investments are innovative. Our strategy here is

collecting all forms of investment and see whether some forms of investment have a greater

share than others at the aggregate level. Then we will explore whether those quantity

differences if any can be justified by differences in the return of the different investments

4

at the firm-level. Since we will find a quantity anomaly between R&D and other forms of

intangible investment, and that these investments have a larger impact on market values in

innovative sectors, we will concentrate on the size and impact of innovative investments.

We briefly discuss first the theoretical framework underlying this approach, and then we

proceed in detail with a broad measurement of recent investment in the Spanish economy.

The findings below will motivate the estimation of the market value–tangible/intangible

capital relationship in the following section.

2.2 Theoretical background: growth models and factor accumulation

The theoretical background we consider justifies the importance of a broad measurement of

investment, and the importance of investment under the hypothesis of Embodied Technical

Progress (ETP). Prior to the idea of ETP, we start from neoclassical growth theory (a

la Solow) to arrive to the consideration of knowledge(-varieties) as the way to endogenize

technical progress.

In a seminal contribution, Robert M. Solow (1957) suggests a procedure to measure

the contribution to economic growth of factor accumulation and technical progress, in a

consistent way with Neoclassical growth theory. Aggregate output is assumed to be produced

according to a Cobb-Douglas technology

Yt = AtK�t L

1−�t , (1)

where Yt is output, Kt and Lt are capital and labor allocated to production, respectively, and

At is the state of technology, the so-called total factor productivity (TFP). Consequently,

the growth rate of technical progress gA can be measured as

gA = g y − �g k. (2)

where g y is the growth rate of output per worker (or hour worked) and g k is the growth rate

of the per worker stock of capital. Observed measures of GDP and the labor input, as well as

constructed measures of the capital stock are usually used to identify technical progress. In

5

order to measure the capital stock, the Neoclassical theory assumes that output is allocated

to consumption and investment according to

Yt = Ct + It,

and capital accumulates following the method of permanent inventories

Kt+1 = (1− �)Kt + It,

where past capital depreciates at the rate �, � > 0. Data on investment from national

accounts are then used to build capital series.

The key issue is the proper measurement, as well as a proper understanding of TFP

growth. The Solow procedure correctly identifies the sources of growth if output and inputs

are correctly measured, the technology is Cobb-Douglas and the measure is exogenous to the

time and place.

Attempts to endogenize the rate of technical progress relate to considering several forms

of technology accumulation underlying the At process. Approaches that have been used to

this purpose consider external effects and knowledge spillovers. Also there are several exten-

sions to two-sector models that exploit the existence of either more than one accumulable

factor (R&D, human capital, intangible capital) or more than a source of technical progress

(embodied or disembodied).4

According to Romer (1990), for instance, R&D activities accumulating in knowledge

allow for the progress of TFP.5 Output from (1) can be allocated to consumption, Ct, the

accumulation of capital, It, and R&D investment, Xt, according to

Yt = Ct + It +Xt.

4Rather than accounting for technology accumulation, an alternative route leads to growth models withTFP differences. This later theory is possibly needed to account for differences in international income (seeParente and Prescott (2000)).

5Contrary to models of external effects, growth is not a side product of factor accumulation, but is aresult of profit-maximizing firms’ intentionally investing in R&D. In this framework, to make R&D activitiesprofitable, researchers have the right to patent their discoveries, which give them some monopoly power.

6

The R&D technology creating new goods or improving the quality of existing goods makes

TFP grow according to

At+1 − At = bXt

Yt, (3)

where parameter b > 0 gives the marginal productivity of R&D production. The larger the

fraction of resources allocated to R&D is, the large is technical progress and growth. In this

setting, TFP growth depends on intangible capital accumulation, which takes the particular

form of innovative investments in R&D. Whether TFP, At, is a function of “fundamental”

research or total business spending in intangibles is mostly a measurement issue, possibly

sector-specific.

One fundamental problem with the Neoclassical and the endogenous growth models is

that they are inconsistent with the observation of a secular decline in the relative price of

investment goods. The model can be extended so that it matches this secular decline by

introducing an equipment investment good sector and assuming that technological change

is faster in this sector (see Grenwood et al. (1997)). Therefore, in the framework of the

Neoclassical growth model (intangible investment X may be easily added to the analysis),

let us assume that

Ct + Zt = BtK�t (Lt)

1−�, (4)

where now Bt represents the state of technology in the consumption goods sector, call it

neutral (or disembodied) technical progress, and Kt is a measure of effective capital whose

meaning will become clear below. Different than the Neoclassical model, Zt is not investment

but an input in the technology to produce equipment according to

It = qtZt, (5)

where qt is a measure of technical progress specific to the investment goods sector, that is a

measure of embodied technical progress, and It is the flow that builds effective capital Kt,

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according with

Kt+1 = (1− �)Kt + It.

In this setting, an economy that does not invest (in equipment) does not get the rewards

from this second form of technical progress. This economy produces then two different final

outputs, consumption and investment, using two different technologies with different rates of

technical progress, that we denote gB and gq. The growth rate of technical progress, denoted

gA as before, is a linear combination of the growth rates of both sectors, weighted by their

contribution to total output,

gA = (1− s)gB + sg q. (6)

where the share of consumption and investment in output are denoted 1− s and s, respec-

tively.

It is in this framework that the construction of a broad measure of investment is pursued,

and assessed to account for the importance of different types of capital for the creation of

value at the firm-level.

2.3 A broad measurement of investment

Investment can be defined as any allocation of resources designed to increase future produc-

tion possibilities. Among tangible investment, new structures and equipment together with

the changes in inventories are measured, whereas resources devoted to maintenance and re-

pair go expensed. As discussed above, process innovation is notably related with equipment

investment, and the embodiment hypothesis provides a rationale for this. Among the sources

for Intangible investment we have R&D and Software that, if internal to the firm, either go

expensed or they are measured as an intermediate input. In addition, organizational capital

goes typically unmeasured, as it is the case with the most part of human capital accumu-

lated within the firm and sweat equity. Product innovation and organizational innovation

(see Black and Lynch (2005)), but also some forms of process innovation are closely related

to all these forms of investment.

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(Private Business) Fixed Investment

The National Accounts of the Spanish economy classifies Private Business Fixed Investment

in Tangible Fixed Assets, as Structures (residential and non-residential), Equipment and

Cultivated Land and Cottage, and Intangible Fixed Assets, as Computer Software, Mineral

Exploration and Creative (leisure, literary, arts) Property, and other. In addition, (Big)

Repairs of (non-produced) tangible assets (land) and Transfers for (non-produced) tangible

assets (land, patents) are also measured as fixed assets.

Table 1 reports fixed tangible investment according to the categories collected in National

Accounts and with special emphasis on the equipment investment figures. The top panel

reports the figures in levels, and the bottom panel in percentage of GDP.

[INSERT TABLE 1 ABOUT HERE]

Fixed Investment

Table 2 reports fixed intangible investment according to the categories collected in Na-

tional Accounts in levels and in percentage of GDP.


Intangible Investment

The value added by reporting these figures is small. The reported results serve to provide

a broad description of measured investment. The more relevant findings can be summarized

as follows. Gross Fixed Investment in the Spanish economy has moved from about 20% of

GDP in 1995 to above 30% in 2005. It has been reported elsewhere that on average it was

about 22% along the period 1964-95 (cf Estrada et al. (1997)). This remarkable increase

observed in recent decades comes mostly from residential investment which has moved from

4% to nearly 10% of GDP. Over the recent period equipment investment has remained

relatively constant at about 8% of GDP (in the manufacturing sector this ratio is 20%), and

it has exhibited a nominal annual growth rate at about a 3.5%. Investment in other fixed

intangible assets registered in National Accounts represents about 4% of fixed investment,

9

that is, nearly 1% of GDP. This intangible investment figures mainly correspond to software

acquisitions, and represent a small fraction of intangible assets in the Spanish economy as

we document below.

(Private Business) Spending on Intangibles

To asses the economic significance of the fixed investment figures just reported, and to give a

scope for how much of investment goes unmeasured or is not taken as such (goes expensed)

in the Spanish economy, we build an estimate for the values of intangible investments in

the Spanish economy from several sources. We follow Corrado, Hulten and Sichel (2005)

in characterizing intangible investment along the following three categories: Computerized

Information, Scientific and Creative Property and Innovative Property, and Economic Com-

petencies. Next we briefly revise the content of these categories. Some further details can

be found in Corrado et al. (2005), in its application to US data, and in Licandro and Puch

(2008) in the Spanish case.

∙ Computerized Information

Computerized Information is defined as the knowledge capital embodied in computer

software and computerized databases. More precisely, it is organized between

1. Computer Software

Among intangible computer software, the estimated costs of software created by

firms for their own use is considered. Software purchased external to the firm it

is already considered as fixed investment in the Spanish National Accounts (NA

– SAE 93: CNAE95).

2. Computerized Databases

Spending in computerized databases refers to subscription costs to external or

customized databases. These costs are not capitalized in NA, and typically rep-

resent a small figure. In the Encuesta Nacional de Servicios (EAS, 1998-2005),

the sector of IT services is considered in detail (sections 4.1 and 5.15).

Corrado et al. (2005) call attention on the possible double-counting of software, and the

overlap between figures for own-account software and the data on R&D expenditures.

10

Our strategy here is to report software expenditures as belonging to the measure of

internal R&D that it is examined inside the next block (categories 3., 4. and 6.

below). Therefore, Table 3 reports only (for completeness) our estimates for the volume

of Computerized Databases spending as documented in the EAS, 1998-2005, both in

levels and in ratios over GDP.


Computerized Information

The results summarized in the table are not very informative at this stage since we are

leaving the software block apart, included in the R&D data. The value of Computerized

Databases has remained fairly stable at about 0.2% of GDP, slightly increasing over

the period though.

∙ Scientific and Creative Property, and Innovative Property

This block includes scientific and nonscientific R&D, the second category related to

artistic and innovative knowledge embedded in commercial copyrights, licenses and

designs. This second category is not very well measured, in contrast to the scientific

component embedded in patent, licenses and general know-how (not patented).

3. Scientific and Engineering R&D

This category includes R&D expenditures in manufacturing, as well as those in

the software and the ICT industries. We approximate these figures building upon

the data reported in the Estadıstica sobre Actividades de I+D (EI+D), Encuesta

de Innovacion Tecnologica (EIT) and Indicadores de Alta Tecnologıa (CNAE93

section D–manufacturing, and industries 64(2)–ICT, and 72(2)–software).

4. R&D in Finance and other Services

R&D expenditures in the financial sector and other services sectors. We approx-

imate these figures from the EIT and PITEC data (sections J and K –financial,

sectors 742–services in architecture engineering, and 743–technical services, and

the part of R&D in social sciences, division 72).

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5. Mineral Exploration

R&D expenditures in mining, collected again from the EI+D and the EIT (sec-

tion C, sectors 10 to 14 and estimates for subsectors 45112, 7420(3)-(4)–land

exploration and topography services).

6. Copyright and License Costs

With category 4. above, this one completes the nonscientific R&D. Here, R&D

in the literary and artistic edition industry as well as the broadcasting industries.

We deduct this part from category 3. (edition industries from division 22, plus

sectors 921 and 922 and additional information collected in the EAS, formerly–

1997– actually the Survey of Broadcasting Services).

Table 4 reports our estimates for the volume of Scientific and Creative Property, and

Innovative Property in the Spanish economy, in levels, whereas Table 5 reports the

figures in ratios over GDP.


Scientific and Creative Property, and Innovative Property (Levels)

Total spending among these categories doubled from 1998 to 2005, which implies a

nominal growth rate of these spending close to 10% over these years. We complete the

EIT data with recent data obtained from PITEC, for which we associate the fraction

of investment surveyed in the EIT that the panel extract in the PITEC is retaining.

The overall picture is close to the one for R&D in Science and Engineering. The rest

of the categories are only recently available and in many cases are only considered in

the PITEC.


Scientific and Creative Property, and Innovative Property (Ratios over GDP)

Total spending along this category, which is somewhat an augmented R&D record for

industries and services other than the R&D producing industry, reaches nearly a 1 per

cent of GDP. This number roughly coincides with the total R&D figures for Spain, so

12

what it is added here different from R&D, it is compensated with what it is left out

because already measured in value added. It turns out that this figure is in a ratio

1 to 5(-8) to the comparable estimate in Corrado et al. (2005) for 1998-2000 in the

US. This result is hardly explained by the relative position of the Spanish economy to

the US itself. We could roughly characterize this position in relative terms (so leaving

apart the US is 10 times bigger) by two features: a) the Spanish economy is 70 per

cent as rich in income per capita (at international prices), b) this results on average in

a one half technology intensity compared to the US. This two elements would justify,

say, a 1 to 3 comparison with respect to the US.

∙ Economic Competencies

This category is possibly the most difficult to measure and includes brand equity, firm-

specific human capital and organizational capital. The total for these categories is

above 2 per cent of GDP during the 2000-2005 period.

7. Brand Equity

Investments in this category include expenditures on advertising and market re-

search. This covers the costs for launching new products, the creation of cus-

tomers’ lists, and the maintenance of the value of brand names. Both the EIT

and the PITEC collect these data. There is also relevant information in the

EAS in this respect, and there is the data from the Encuesta sobre Estrategias

Empresariales (ESEE) that could be used to complement these figures.

8. Firm-specific Human Capital

This category includes the costs of developing workforce skills. EIT and PITEC

collect these data when corresponds to workers directly engaged in innovative

activities. Again, for a more general estimate the ESEE data could be also used

here.

9. Organizational Structure

For an estimate of investments in the organizational structure information on

executives’ wages and management consulting fees are used. Both of these data

are collected in the EAS, section 5.20.

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Table 6 reports our estimates for the volume of Economic Competencies in the Spanish

economy, in levels, whereas Table 7 reports the figures in ratios over GDP.


Economic Competencies (Levels)


Economic Competencies (Ratios over GDP)

This last category constitutes a traditionally important component of intangible in-

vestment. In levels it has been steadily growing since 2000. However, its share of

GDP seems to have been downsizing mostly along the brand equity and developing

workforce skills categories, and less with respect to organizational capital. Of course,

these numbers are possibly the more subjected to further revision.

Summarizing, there is a lot of value added in the intangibles block of our exercise. First,

we have searched for the sources of these data in the Spanish economy. Second, we have

selected the more reliable data and we have put them together in a comprehensive presen-

tation. Finally, the quantities reported are of interest for many related applications. The

main results can be summarized as follows. Business spending on intangibles in Spain was

about EUR 22 billions in 2000, and above EUR 25 billions in recent years annually, to reach

up to 3.5 per cent of GDP. The absolute figure can be compared to an aggregate level for

the US of about $1.2 trillion (cf. OECD (2008) as well) which can be obtained from the

Spanish one ≃ ×10×2×1.4, with these factors corresponding to size, income and technolog-

ical differences, respectively. In particular, the latter factor relates to Eaton and Kortum’s

(1999) idea of international technology diffusion, and the differences in the contribution to

the technological frontier of some countries with respect to others. The intensity figure can

be compared with a 13 per cent in the US, and relative to other Spanish data with the 7

to 9 per cent of GDP in machinery, and the 0.7 to 0.9 per cent in R&D. Since 2000, and to

2005 we do not observe (yet) major changes in this ratio. One question is whether there are

any major changes to be detected soon. Scientific and Creative Property (R&D) items seem

to have increased at rates above 10 per cent annually, to represent from about one fourth of

14

the estimate for Economic Competencies in 2000, to reach nearly one half of this tradition-

ally important component of intangible investment. However, the R&D figures remain still

in a ratio, say, one to five relative to the comparable figure for the US, whereas the total

spending over GDP for intangible investment is closer to a more interpretable 1 to 3 ratio

with respect to the estimates reported by Corrado et al. (2005) for the US. One may wonder

whether apart from the process of creation of innovations (mostly R&D spending), techno-

logical progress requires the same relative spending in intangibles, so far as the economy is

mostly involved in the process of adoption of innovations.

3 Innovation and Value Creation at the Firm Level

3.1 Theoretical background and the empirical model

The theoretical framework for market value analysis can be derived from a standard dynamic

optimization problem for the firm. The value of the firm at time t, V , can be expressed in

the form of Bellman equation according to

V (A,K) = maxK′

Π(A,K) + �EA′∣AV (A′, K ′)

where A is the state of technology, K is the aggregate stock of capital that evolves to build

next period capital stock K ′ from the flow of investment, and the undepreciated part of the

different types of capital, according to

K ′ = I +∑j

!jkj, all j

and Π is the instantaneous flow of profits at t

Π(A,K) = A F (K,N)−WN − I

where N is labor and W the real wage rate. With constant returns to scale, all assets

documented, and no adjustment costs, buying a firm is equivalent to buying a collection of

separate assets. Therefore, the market value of the firm can be expressed as an additive

15

function of its single assets (cf. Hall (1993))

V (A,K) =J∑j=1

kj, (7)

for A being set to its unconditional mean. Further, whenever a vintage z plant (a plant of

age � = t− z) can be described as

Qt(�) = At(�)F (�t(�), Nt(�))

where

I(t) = �t+1(0)

�t+1(�) = (1− �)�t+1(� − 1).

With constant returns to scale, all plants with the same Total Factor Productivity at t and

the same number of plants for all t (cf. Baily (1981)),

V (A,K) =∞∑�=0

qt−��t(�) ≡ K (8)

for A being set to its unconditional mean. Therefore, under standard regularity assumptions

(zero adjustment costs) and perfect capital markets either heterogeneity by types of capital

or by ages of capital are aggregated out.

and Econometric Model

Theory (market value equations (7) or (8)) suggests a basic estimating relation between the

market value of firm i and the j assets the firm possesses, allowing for repeated observations

over time t

Vit = �i +J∑j=1

�jKj,it + "�it (9)

We would expect �i = 0 all i and � = {�j = 1, all j} under the ideal conditions for

market value equations (7) or (8). However, � deviates from 1 if adjustment costs are

16

present, or omitted variables are correlated with observed assets. We follow Brynjolfsson

et al. (2002) in interpreting �j − 1 as the difference in value between kj type of capital

installed into the firm and otherwise identical capital available in the market. Descriptive

results for our sample in this stylized theoretical framework are discussed below. We also

consider additional covariates, and we deal with firm and time-specific effects to address

factors specific to individual firms. Further econometric issues as sample-selection bias or

the problem of omitted variables are preliminary discussed in Lacuesta et al. (2008). Finally,

we follow Hall and Oriani (2006) in an alternative formulation of equation (9) that makes

explicit that the � coefficients are not structural parameters but equilibrium outcomes in

the market at time t, and therefore, they has to be interpreted as a measure of the current

average marginal shadow value of an additional currency unit spent on capital asset j.

Data

All the accounting data of firms that we use in this part of the analysis come from the

Central de Balances of the Bank of Spain. This database includes financial statements for

an unbalanced panel of non-financial Spanish corporations (consolidated accounts from firms)

which is available at the Bank of Spain. Our sample consists of 2872 traded and non-traded

firms and 20575 observations over the period 1992-2005. Further details on the selection of

the sample can be found in Lacuesta et al (2008). In particular, it was in 1992 that a reduced

questionnaire was introduced, together with a new, more homogeneous accounting regime.

Also, further descriptive statistics of the sample, with applications to the energy sector, are

presented in Licandro and Puch (2008).

The database includes the treatment of the asset position (capitalization) of the firm

according to well established accounting principles. To the book value of tangible and in-

tangible assets the Bank of Spain associates a record of the market value for traded firms,

together with an estimate of the value of equity and other shares for non-traded firms (see

B de E 2005). This makes the selected database a unique source of data for market value

analysis.

Our dependent variable is the market value either reported, for traded firms, or estimated

for non-traded firms by the Bank of Spain. The explanatory variables are the book value

17

of different asset that conform the total asset position reported by firms. Consequently, our

quantitative results are based on raw data without further refinements. The kind of refine-

ments needed to make available the corresponding data for several countries are thoroughly

discussed for instance in Hall and Oriani (2006). These refinements could be adapted to our

sample as a robustness check. In particular, we follow closely the strategy proposed by these

authors in examining the market valuation of different intangible assets, as discussed below.

3.2 Key Results: Market Value and Asset Quantities

We regress market value on book values for the aggregates of tangible and intangible assets.

We do this for stock market, quoted and non-quoted corporations. We also proceed with a

finer disaggregation of those assets reported in the balance sheet at the Central de Balances

level.

There are several econometric problems to be addressed with these data. Among them,

we mostly deal with firm and time-specific effects. We also instrument the explanatory

variables to test for endogeneity. Sample-selection bias, the problem of omitted variables

and other econometric issues are further discussed in Lacuesta et al. (2008).

Investment and Market Value: Whole Sample

Table 8 reports results of regression analysis for the relationship between different types

of assets and market value, all variables are in nominal terms, in millions of Euros. This

regression includes physical tangible assets (equipment and structures), intangible assets

(principally, capitalized R&D expenditures, industrial property and other intangible assets)

and computer assets. Software is treated independently below, since it is only available after

2001. We also include measures of labor productivity and debt to assets ratios as controls

whose coefficients are reported if they are significant.


Market values on Asset Quantities

The first column in Table 8 reports the results when tangible and intangible assets are

18

the only explanatory variables. According to these, each euro of value of tangible assets

explains EUR1.2 of market value. On average, intangible assets explain five times more of

the market value of the firm than the value of tangible capital. The second column shows

further that a major component of tangible capital are the computer assets. When computer

assets are treated independently from the rest of tangible assets, each euro of computer assets

is associated with about EUR15 of market value. This apparent excess sensitivity of market

value to computer assets may suggest the presence of adjustment costs or other omitted

components of market value correlated with these assets.

To more precisely account for the valuation of computer assets we consider a measure of

other assets, computed as the difference between the book value of total assets and the sum of

tangibles and intangibles. Column (3) attributes about EUR1.15 of market valuation to each

euro of these additional assets, with the contribution of both tangible and intangible assets

to value being slightly qualified with respect to the base case (1). Taking these additional

assets into account (column (4)) we find that the market value of computer assets is close to

EUR10, whereas the coefficient of other assets component is essentially the same. The result

that the market valuation of intangible assets is five times bigger than that of equipment

and structures is quite stable across specifications.

The finding that the coefficient for computers is about ten, whereas other types of cap-

ital receive coefficients below one, does not reflect that investment in computers earns an

excess return. On the contrary this finding reveals a strong correlation between the stock of

computer assets and unmeasured and much larger stocks of intangible assets.

The role of the controls is limited or non-significant across regressions. Because we are

pooling multiple firms in multiple years, in all of the cases we include dummies for each year

and sector at the second digit. Further, time and industry controls are jointly significant, so

we are able to remove temporal shocks and omitted components relative to time period and

industry. Note however that the coefficients for 2004 and 2005 year dummies are significant

and sizeable (not shown). We use robust standard errors that are reported in parentheses.

Indeed, various types of firm-specific assets or organizational practices that are time-

invariant can contribute strongly to the market value of the firm. One way to account for

these assets is a fixed effects (FE) estimation of the market value equation. Column (5) in

19

Table 8 suggests that once we remove the contribution of any time-invariant, firm-specific

component of market value the valuation of computer assets is essentially the same, but now

significant only at 5%. On the other hand, the variability in market value seems to remain

significantly explained, and closely in a one to one ratio, by the measure of other assets.

The result obtained in the pooled sample that each euro in intangible assets contributes

to market valuation five times more than each euro in non-computer tangible assets is non-

significant under fixed effects. In this respect, it is worth noting that market values typically

capitalize long-run characteristics of firms. Therefore, conditional on fixed effects, it is not

surprising that annual realizations of tangible and intangible assets do not have a significant

role in affecting market values. This turns out to be the case once computer assets are

introduced separately into the analysis, and as far as the coefficient for these assets has a

sizeable valuation.

One way to account for time-invariant firm-specific component of market value over

moderately long periods is by estimating a difference specification. Varying difference lengths

allows comparison of short-run (pool) and long-run (fixed effects) relationships. Table 9

reports estimates for the benchmark case (4) for intermediate difference specifications. The

relationship for intermediate differences seems substantial for computer assets, and it is

somewhat preserved with respect to the pool for intangible and other assets. We conclude

that it is the effect of tangible assets on market valuation what it is less precisely measured

in the whole sample.


Market Values on Asset Quantities: Long Differences

Therefore, ordinary least squares estimates of the pooled sample seem to be robust to

long differences. Also, model incorporating firm-specific fixed effects essentially preserves

the significant impact of computer assets and the explanatory role of the variability in other

assets. Finally, the results are robust to instrumental variable estimation over lagged ex-

planatory variables. Whereas the variable lagged intangible assets is non-significant, last

column in Table 8 reports two-stage least squares estimates when the variable lagged other

assets is incorporated. In this case, the results are essentially preserved. Moreover, the

20

impact of tangible and intangible assets seems more precisely measured when compared to

fixed-effects estimation. Some other robustness checks are available upon request.


Market Values on Asset Quantities, by Sector of Activity

Finally, we explore what are the differences between sectors for the benchmark regression

equation (4). In Table 10 sectors are ordered from those for which a higher weight of

intangible assets could be expected to those one might expect a stronger link of tangible

assets to market values. The results clearly confirm the economic intuition and investment

patterns are ordered across sectors. The second column retains the result for the whole

sample. The label High Technology captures a broad sample of high and medium technology

sectors. Again, a strong correlation between the stock of computer assets and unmeasured

assets is found for this group of sectors. The energy sector for instance exhibits the higher

estimate for the value of tangible assets contribution to market values. However, the way it

compares with the building sector is very much in favor of the energy sector in terms of the

role of intangible values, and thus of innovative activity according to our interpretation. The

results under fixed-effects by sectors can be interpreted in line with the discussion above for

the pool. Next we proceed with a finer disaggregation of assets.

Investment and the Market Value of R&D

As indicated above, finer disaggregation of intangible assets is possible since 2001. This

disaggregation involves capitalized R&D expenditures, industrial property (patents, good

will) and software expenditures. Here, we build upon the previous results to focus on R&D

capital. To this purpose we follow Hall and Oriani (2006) in expressing all the alternative

assets relative to the tangible assets of the firm. This strategy allows us to account for

the relative size of intangible assets at the firm level, at the same time we disregard the

asset component whose valuation seems to have a more limited impact in the value of the

firm. More precisely, according to these authors, a version of econometric model (9) can be

rewritten as

log(Vit/Ait) = logqt + log(1 + Kit/Ait + �Iit/Ait) + 'it (10)

21

where the ratio V/A is a proxy for average Tobin’s q, the ratio of the market value of the firm

to the book value of tangible assets, and thus qt reflects the average market valuation of a

firm’s total assets. K is the value of R&D capital and I is the value of other intangible assets

and consequently, and � are the relative shadow values of these assets to tangible assets.

With the approximation log(1 + x) ≃ x we can estimate (10) by OLS, in a specification

closer to model (9) which is in levels and assumes no adjustment costs. Otherwise we

report estimates by NLLS, and the corresponding slope coefficients comparable with the

OLS estimates.

We perform regressions of market values on asset quantities for the different categories

of intangible assets. The more robust results are obtained when we examine the market

value of the firm as an indicator of the firm’s expected result from investing in R&D. Notice

that we restrict the sample to those firms declaring to be engaged in R&D activities. Hall

and Oriani (2006) perform separate regressions for each group of firms and they find that

R&D disclosure is closely related to firm size. Table 11 reports estimates for regressions

of market value to tangible assets ratio on the corresponding ratios for R&D on the one

hand, and other intangible assets on the other. The regressions controls for the log of sales.

The first column reports the OLS estimates for these assets, and suggests that each euro

invested in R&D contributes to market valuation of the firm about three times more than

the rest of intangible assets. Further, the coefficient is not significantly different from the

equilibrium value of unity. These results are in line with those reported for the UK by Hall

and Oriani (2006), which represents the country case most favorable for market valuation

of R&D capital among the countries these authors consider: US, UK, France, Germany and

Italy.


Market Values on Asset Quantities: R&D

The next two columns report the results of the NLLS estimation. The relevant parameter

is now the coefficient divided by one plus the weighted average of the capitals. The result

of computing that ratio at the variable is shown below the corresponding estimate, and the

result of averaging the estimated coefficient for each firm is shown below that. These values

22

are lower than both the OLS and NLLS estimates, which suggests the linear model places

too much weight on large levels of R&D capital. Even with this alternative specification,

the estimates can be taken as evidence that market valuations reflect a very positive impact

from R&D capital of Spanish firms. Possibly, the main circumstance underlying these results

is that R&D activities exhibit a particularly high correlation with size and the presence in

the stock market, for the Spanish sample of firms compared with other countries. Also,

estimates should be taken with caution because of the limited number of observations in our

sample.

Investment and Market Value: Non-Quoted Firms

We have tried to explore in further detail the role of asset quantities for market valuation for

firms publicly traded in the stock market and the rest of firms. Theory predicts intangible

capital worths more for quoted firms because these assets are more important for equity

holders, whereas tangible capital worths more for non-quoted firms because of debt holders.

However, the number of traded firms in our sample is too tiny to figure out reliable results.

We alternatively explore the market value properties of the subsample that excludes

traded firms. Therefore, we can compare the results for the whole sample in Table 8, with

these additional estimates in Table 12, for the subsample of non-traded firms. The results in

general do not change much. Actually, the valuation of firms in relation to their measured

intangible capital increases. This finding is possibly revealing a strong correlation between

the stock of measured intangibles and unmeasured and larger stocks of intangible capital.

As stated in Hall (2001), it is not that the market values a euro of measured intangibles at

10 euros. Rather, the firm that has a euro of measured intangibles typically has another 9

of related unmeasured intangible assets. Finally, the fixed effect regression is not precisely

estimated but for the effect of the additional other assets on the value of the firm, that

apparently have even larger variability in annual realizations for this subsample.


Market Values on Asset Quantities: Non-Quoted

Summarizing, the results suggest that intangible investments have a positive impact on

23

market values which is more substantial for innovative sectors, and this is also the case for

R&D capital. Such a positive impact is influenced by the size of the firm and its presence

in the stock market. An alternative explanation to low R&D intensity could be found in the

small fraction of firms publicly traded in the stock market in Spain rather than in any small

returns. It is commonly understood that new firms in innovative sectors with a expansionary

potential have the stronger incentives in going publicly traded. This seems to be the case

more in the US and UK than in continental Europe notwithstanding. Consequently, the

evidence obtained from market values for innovative sectors in Spain suggests that promoting

a more active role of market valuations as a guide for innovative investment might be a

promising policy.6

4 Discussion and Concluding Remarks

A number of recent papers have examined the quantity, and the role of intangible investments

for the US Economy. Part of these papers have focused on the market valuation of those

investments. Here, we have addressed questions related to the measurement and valuation

of the holdings of various types of capital for the Spanish economy. We have done this with

an emphasis on innovative investments and notably on R&D capital.

In assessing the investment position of the Spanish economy in recent years the main

finding is the relatively low level of R&D investment. This is not new. The striking result is

that this figure seems to be relatively low compared to other intangible capital investments.

Nevertheless, we find that the aggregate block of Scientific and Creative Property and In-

novative Property is exhibiting the higher growth rates in recent years among the different

categories of intangible investments.

It is worth noting that the aggregate measure of intangible investment we report for the

Spanish economy is roughly consistent with the economic and technological gap we could

more generally observe relative to the US economy. We can interpret this finding as if

technological advances would require the same spending on intangibles irrespective of the

degree of R&D intensity in the economy. This amounts to say that the technology adoption

6See Farinos and Sanchıs (2009) and the references therein on the limited stock market amplitude inSpain, as well as following Pagano et al. (1998) on the reasons why Spanish firms go public.

24

process, compared to the creation process, is relatively more important for the Spanish

economy than it is for the US. The question is then whether the low R&D intensity is an

indicator for poor firm’s expected economic results from innovative investments.

Our assessment for the relative worth of alternative investments uses the market value

of the firms. The Central de Balances database which is available at the Bank of Spain

is a very convenient source of data for this purpose since it pulls together firm’s market

valuations with financial statements of tangible and measured intangible assets. We run

regressions of market values on asset quantities over the period 1992-2005. The main finding

is that the coefficient of the firm’s R&D capital is greater than one in our sample. If we

assume that financial markets are efficient this finding suggests that firms disclosing R&D

expenditure are investing too low, because the value of the assets created worth more than

their cost. Note this result is for the business non-financial sector so that the average return

of R&D investment could be reduced if publicly funded R&D yields lower return. The role for

market valuation of institutions as, legal regimes, the stock market amplitude or ownership

structures is an important issue that is left for further research.

It is worth noting as well, that the finding of a coefficient for an asset which is substan-

tially above unity mostly reveals a strong correlation between the stock of that asset and

unmeasured and much larger stocks of intangible assets. We have revised some of these

candidate assets that can be proxied from various spending data from innovation surveys,

and in sizeable amounts as documented along Section 2, for instance through the new or-

ganizational structure. Some other are relatively missing from our categorization like new

market strategies and related. Finally, market values typically capitalize long-run charac-

teristics of firms. Therefore, conditional on fixed effects, it is hard to find a significant role

for annual realizations of tangible and intangible assets in affecting market values. This and

other relevant econometric issues are left in part for subsequent research.

Interestingly, the patterns for the value of different investments are preserved if assets are

interacted with sectors. Moreover, assets that can be associated to innovative investments

have a stronger impact on market values in high and medium technology sectors. We think

this finding supports the empirical strategy in this paper, and reinforces the interpretation

on the Spanish economy being relatively more involved in technology adoption in recent

25

years. Spanish firms might not be moving the technological frontier indeed, but it is very

important to know how far they are from it. For this, further research is needed on the size,

extent and diffusion in Spain of technologies created by others. Existing and ongoing studies

on R&D activity are crucial to learn on how much Spanish firms will be able to reduce such

a distance with the technological frontier instead. Looking simultaneously to creation and

adoption constitutes a hard but surely promising research agenda.

The results obtained in this study could be of interest for related applications and provide

relevant evidence for the Spanish economy in light of the debate for the Lisbon agenda and

the financing of innovative activities in the European Union.

26

References

Abowd, J.M., J. Haltiwanger, R. Jarmin, J. Lane, P. Lengermann, K. McCue, K. McKinney

and K. Sandusky (2005), “The Relation among Human Capital, Productivity, and Market

Value: Building Up from Micro Evidence.” In Measuring Capital in the New Economy,

C. Corrado, J. Haltiwanger and D. Sichel, Editors, The University of Chicago Press.

B de E (2005), “Valoracion de las acciones y otras participaciones en las Cuentas Financieras

de la economıa espanola.” Dto. de Estadıstica y Central de Balances, Banco de Espana.

Baily, Martin (1981), “Productivity and the Services of Capital and Labor.” Brookings

Papers on Economic Activity: Macroeconomics 1:1981, 137-199.

Black, Sandra, and Lisa Lynch (2005), “Measuring Organizational Capital in the new Econ-

omy.” In Measuring Capital in the New Economy, C. Corrado, J. Haltiwanger and D.

Sichel, Editors, The University of Chicago Press.

Brynjolfsson, Erik, Lorin M. Hitt and Shinkyu Yang, 2002, “Intangible Assets: How the

Interaction of Computers and Organizational Structure Affects Stock Market Valuations.”

Brookings Papers on Economic Activity: Macroeconomics 1:2002, 137-199.

Corrado, Carol, Charles Hulten and Daniel Sichel (2005), “Measuring Capital and Technol-

ogy: An Expanded Framework.” In Measuring Capital in the New Economy, C. Corrado,

J. Haltiwanger and D. Sichel, Editors, The University of Chicago Press.

Doraszelskiy, Ulrich, and Jordi Jaumandreu (2007), “R&D and productivity: Estimating

production functions when productivity is endogenous,” mimeo.

Eaton, John and Samuel Kortum (1999), “International Technology Diffusion: Theory and

Measurement” International Economic Review 40(3): 537-570.

Estrada, Angel, Francisco de Castro, Ignacio Hernando y Javier Valles (1997), “La Inversion

en Espana.” Banco de Espana, Estudios Economicos, n 61.

Farinos, Jose E. and Vicente Sanchıs (2009), “Factores Determinantes de la salida a Bolsa

en Espana,” WP2009-03, IVIE.

27

Griliches, Zvi (1979), “Issues in assessing the contribution of R&D to productivity growth.”

Bell Journal of Economics 10(1), 92-116.

Hall, Bronwyn (1993), “The Stock Market’s Valuation of R&D Investment during the

1980’s.” American Economic Review (Papers and Proceedings) 83(2): 259-64.

Hall, Bronwyn, and Raffaele Oriani (2006), “Does the Market Value R&D Investment by

European Firms? Evidence from a Panel of Manufacturing Firms in France, Germany,

and Italy.” International Journal of Industrial Organization, forthcoming.

Hall, Robert E. (2001), “The Stock Market and Capital Accumulation.” American Economic

Review 91(5):1185-1202.

Jovanovic, Boyan (1997), “Learning and Growth,” in Advances in economics and economet-

rics: theory and applications, Vol. II,Kreps and Wallis eds. Econometric Society.

Lacuesta, Aitor, Teresa Molina and Luis A. Puch (2008), “Intangible Capital, the stock

market and the Value of Spanish Firms,” mimeo.

Licandro, Omar, Reyes Maroto and Luis A. Puch (2005), “Innovation, Machine Replacement

and Productivity: Evidence from Spanish Firms.” DP 5422, CEPR.

Licandro, Omar and Luis A. Puch (2008), “Measurement and Value of Innovation in Spain:

A Contribution to the Energy Debate.” Focus-Abengoa Report, mimeo.

McGrattan, Ellen, and Edward C. Prescott (2005), “Taxes, Regulations, and the Value of

U.S. and U.K. Corporations.” Review of Economic Studies 72, 76796.

McGrattan, Ellen, and Edward C. Prescott (2006), “Why Did U.S. Market Hours Boom in

the 1990s?” Staff Report 369, Minneapolis Fed.

OECD (2008), “Intellectual Assets and Value Creation,” OECD Synthesis Report.

Parente, Stephen, and Edward C. Prescott (2000), Barriers to Riches. The MIT Press.

Romer, Paul (1990), “Endogenous Technological Change.” Journal of Political Economy

98(5): 71-102.

28

Pagano, Marco, Fabio Panetta and Luigi Zingales (1998), “Why do companies go public?

An empirical analysis.” Journal of Finance 53: 27-64.

Solow, Robert (1957), “Technical Change and the Aggregate Production Function.” Review

of Economics and Statistics 39, 312-320.

Solow, Robert (1960), “Investment and Technological Progress.” In Mathematical Methods

in Social Sciences, K. Arrow et al. Editors, Stanford University Press, 89-104.

29

Tab

le1:

Fix

edIn

vest

men

t(L

evel

s&

Rat

ios

over

GD

P)

Gro

ssF

ixed

Cap

ital

Form

ati

on

(GF

CF

)M

atr

ixC

urr

ent

pu

rch

ase

r’s

pri

ce(m

illi

ons

ofE

UR

)

Pro

du

cts

1998

1999

2000

(1)

2001

2002

2003

2004

2005

(p)

2006

(a)

Pro

du

cts

of

agri

cult

ure

.li

vest

ock

and

fish

ing

329

331

545

568

559

536

355

378

378

Met

alp

rod

uct

san

dm

ach

iner

y28

420

3092

335

791

3605

635724

36901

39014

42554

48454

Tra

nsp

ort

equ

ipm

ent

9638

1079

215

434

154

7915139

16888

18766

22397

24638

Equ

ipm

ent

good

s38058

41715

51225

51535

50863

53789

57780

64951

73092

%onTotal

31.5

30.6

30.9

28.7

26.2

25.1

24.3

24.2

24.4

Hou

sin

gco

nst

ruct

ion

2530

129

207

3856

0444

4551437

61069

70267

80624

91552

Oth

erco

nst

ruct

ion

s38

248

4323

945

330

5071

456

026

60429

66490

75205

83809

Oth

erp

rod

uct

s18

783

2184

527

146

297

0432726

36977

40913

45466

49363

Tota

l12

0719

1363

3716

5618

1793

85194

188

214399

237806

267938

300036

Sec

tori

alG

VA

4806

4951

1054

5705

60618

252

661517

706932

756669

813434

873703

In%

ofSectoralGross

ValueAdded

atbase

prices

Pro

du

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of

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cult

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.li

vest

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and

fish

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0.1

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96.

16.3

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5.2

5.2

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12.

72.5

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2.5

2.8

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ipm

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good

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98.

29

8.3

7.7

7.6

7.6

88.4

%onTotal

Hou

sin

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5.3

5.7

6.8

7.2

7.8

8.6

9.3

9.9

10.5

Oth

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nst

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s8

8.5

7.9

8.2

8.5

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8.8

9.2

9.6

Oth

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s3.

94.

34.

84.

84.9

5.2

5.4

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Tota

l25

.126

.729

29

29.4

30.3

31.4

32.9

34.3

Note

s.(1

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NE

,b

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.(a

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E(I

NE

)

30

Tab

le2:

Imm

ater

ial

Inve

stm

ent

(Lev

els

&R

atio

sov

erG

DP

)

Imm

ate

rial

invest

ments

on

Gro

ssF

ixed

Cap

ital

Form

ati

on

(GF

CF

)M

atr

ix

Cu

rren

tp

urc

hase

rsp

rice

(mil

lion

sof

EU

R)

1998

1999

2000

(1)

2001

2002

2003

2004

(p)

Min

ing

an

dO

ilR

esea

rch

84.0

461

.77

59.

20

150.

90

76.5

036.8

616.6

2S

oft

ware

3929

.35

4801

.61

5298

.11

6153

.84

6958.5

57915.5

78679.8

6O

rigi

nal

ente

rtain

men

t,li

tera

ryor

art

pie

ces,

630.

5667

3.75

957.4

110

22.1

71080.1

81160.8

21293.1

5an

dot

her

fix

imm

ater

ial

ass

ets

Tota

lIm

mat

eria

lIn

vest

men

t46

43.9

555

37.1

363

14.7

273

26.9

18115.2

39113.2

49989.6

3

Sh

are

onG

FC

F1998

1999

2000

(1)

2001

2002

2003

2004

(p)

Min

ing

an

dO

ilR

esea

rch

0.07

%0.

05%

0.04%

0.09%

0.0

4%

0.0

2%

0.0

1%

Soft

ware

3.34

%3.

62%

3.35%

3.57%

3.7

3%

3.8

1%

3.7

8%

Ori

gin

al

ente

rtain

men

t,li

tera

ryor

art

pie

ces,

0.54

%0.

51%

0.60%

0.59%

0.5

8%

0.5

6%

0.5

6%

and

oth

erfix

imm

ater

ial

ass

ets

Tota

lIm

mat

eria

lIn

vest

men

t3.

95%

4.17

%3.

87%

4.1

3%4.2

3%

4.2

4%

4.2

4%

Note

s.(1

)C

NE

,b

ase

yea

r199

5.F

rom

yea

r20

00,

CN

E,

bas

eye

ar20

00.

(p)

Pro

vis

ion

al

esti

mate

.(a

)A

dva

nce

esti

mate

.S

ou

rce.

CN

E(I

NE

)

31

Tab

le3:

Com

pute

rize

dIn

form

atio

n(L

evel

s&

Rat

ios

over

GD

P)

Data

availab

ilit

yan

dest

imate

dsi

ze

of

bu

sin

ess

spen

din

gon

inta

ngib

les.

by

typ

eof

ass

et

(billionsofcurren

tEUR;billionsofcurren

tUS$)

Typ

eof

ass

et

or

spen

din

gD

ata

availab

ilit

yE

stim

ate

dsi

ze

an

dd

ata

sou

rces

1998

1999

2000

US

98-0

02001

2002

2003

2004

2005

Com

pu

teri

zed

info

rmati

on

1.0

32

-1.1

75

155

1.3

65

1.5

68

1.8

84

1.9

82

1.9

07

1.

Com

pu

ter

soft

ware

Soft

ware

exp

.In

clu

ded

inIn

tern

al

R&

Dfo

rfi

rm’s

own

use

2.

Com

pu

teri

zed

data

base

sK

now

led

ge

in1.

032

-1.

175

31.

365

1.5

68

1.8

84

1.9

82

1.9

07

com

p.

data

bas

es

Typ

eof

ass

et

or

spen

din

gD

ata

availab

ilit

yE

stim

ate

dsi

ze

In%

ofGDP

an

dd

ata

sou

rces

1998

1999

2000

US

98-0

02001

2002

2003

2004

2005

Com

pu

teri

zed

info

rmati

on

0.1

91

-0.1

86

1.6

64∗

0.2

01

0.2

15

0.2

41

0.2

36

0.2

11

1.

Com

pu

ter

soft

ware

Sof

twar

eex

p.

Incl

ud

edin

Inte

rnal

R&

Dfo

rfi

rm’s

own

use

2.

Com

pu

teri

zed

data

base

sK

now

led

ge

in0.

191

-0.

186

0.201

0.2

15

0.2

41

0.2

36

0.2

11

com

p.

data

bas

es

Sou

rce:

En

cu

est

aA

nu

al

de

Serv

icio

s(B

usi

ness

Sp

en

din

g).

(∗)

Th

isfi

gu

rein

clu

des

com

pu

ter

soft

ware

for

the

US

32

Tab

le4:

Sci

enti

fic

and

Cre

ativ

eP

rop

erty

,an

dIn

nov

ativ

eP

rop

erty

(Lev

els)

Data

avail

ab

ilit

yan

dest

imate

dsi

ze

of

bu

sin

ess

spen

din

gon

inta

ngib

les,

by

typ

eof

ass

et

(billionsofcurren


tUS$)

Typ

eof

ass

et

Data

avail

ab

ilit

yE

stim

ate

dsi

ze

or

spen

din

gan

dd

ata

sou

rces

1998

1999

2000

US

98-0

02001

2002

2003

2004

2005

Scie

nti

fic

an

d(4

.087,

-(4

.477,

(325,

(5.0

29,

(5.6

49,

(7.5

20

(7.4

79,

8.3

43

cre

ati

ve

pro

p5.1

79)

4.5

05)

525)

5.0

66)

5.6

96)

7.5

77)

7.5

37)

3.

Scie

nce

an

dR

&D

hig

h&

med

-hig

hE

IT2.2

25

-2.9

69

184

-3.7

86

4.5

83

4.9

06

5.2

79

en

gin

eeri

ng

R&

Dte

chm

anu

fact

uri

ng

int

&(3

.360,

+h

igh

tech

serv

ices

(a)

ext

(b)

4.4

52)

PIT

EC

--

--

-3.9

37

4.0

20

4.9

65

(%IS)

--

--

-85.9%

81.9%

94.0%

+O

ther

man

uf

+E

IT0.7

21

-0.7

65

-0.6

70

0.9

70

1.0

24

1.1

77

Ele

c,G

as&

Wat

erP

ITE

C-

--

--

0.7

02

0.6

77

0.9

87

(%IS)

--

--

-72.4%

66.2%

83.9%

4.

Min

era

lS

pen

din

gfo

rth

eE

IT0.0

06

-0.0

07

16

-0.0

09

0.0

17

0.0

13

0.0

13

exp

lora

tion

acqu

isit

ion

ofP

ITE

C-

--

--

0.0

13

0.0

08

0.0

10

new

rese

rves

(c)

(%IS)

--

--

-75.2%

61.7%

71.7%

5.

Copyri

ght

&In

info

rmat

ion

-sec

tor

EIT

--

0.1

13

(50.

-Under

construction(INE)

licen

secost

sin

du

stri

esP

ITE

C-

--

100)

--

0.0

28

0.0

30

0.0

27

(pat

ent

orli

cen

se)

(%IS)

--

--

-6.

Oth

er

pro

du

ct

R&

Din

fin

ance

&E

IT-

-(0

.252,

(75,

(0.4

30,

(0.5

32,

(0.4

66,

0.5

47

develo

pm

ent,

oth

erse

rvic

es(d

)0.2

66)

224)

0.4

54)

0.5

60)

0.4

95)

desi

gn

&P

ITE

C-

--

--

0.4

06

0.3

02

0.5

29

rese

arc

h(%

IS)

--

--

-(72.6%,

(60.95%,

97%

exp

en

ses

76.4%)

64.72%)

+R

est

ofE

IT-

(0.2

88,

(0.7

13,

(1.2

63,

(0.7

88,

1.1

17

Ser

vic

es0.3

02)

0.7

36)

1.2

92)

0.8

17)

PIT

EC

--

--

-0.1

82

0.2

13

0.3

54

(%IS)

--

--

-(14.1%,

(26.1%,

0.315

14.4%

)27.0%)

+C

onst

ruct

ion

EIT

--

0.0

83

-0.0

41

0.1

56

0.2

82

0.2

10

PIT

EC

--

--

-0.0

42

0.0

47

0.0

81

(%IS)

--

--

-26.9%

16.8%

38.6%

33

Tab

le5:

Sci

enti

fic

and

Cre

ativ

eP

rop

erty

,an

dIn

nov

ativ

eP

rop

erty

(Rat

ios

over

GD

P)

Data

avail

ab

ilit

yan

dest

imate

dsi

ze

of

bu

sin

ess

spen

din

gon

inta

ngib

les,

by

typ

eof

ass

et

(In%

ofGDP)

Typ

eof

ass

et

Data

avail

ab

ilit

yE

stim

ate

dsi

ze

or

spen

din

gan

ddata

sou

rces

1998

1999

2000

US

98-0

02001

2002

2003

2004

2005

Scie

nti

fic

an

d(0

.758,

-(0

.692,

(3.4

90,

(0.7

39,

(0.7

75,

(0.9

61,

(0.8

90,

0.9

21

cre

ati

ve

pro

p0.9

60)

0.6

97)

5.6

37)

0.7

44)

0.7

81)

0.9

68)

0.8

97)

3.

Scie

nce

an

dR

&D

hig

h&

med

-hig

hE

IT(b

)0.4

13

-0.4

71

-0.5

19

0.5

86

0.5

84

0.5

83

en

gin

eeri

ng

R&

Dte

chm

anu

fact

uri

ng

(0.6

23,

+h

igh

tech

serv

ices

0.8

25)

(a)

PIT

EC

--

--

-0.5

03

0.4

78

0.5

48

+O

ther

Man

uf

+E

IT0.1

34

-0.1

21

-0.0

92

0.1

24

0.1

22

0.1

30

Ele

c,G

asan

dW

ater

PIT

EC

--

--

-0.0

93

0.0

79

0.0

93

4.

Min

era

lS

pen

din

gfo

rth

eE

IT0.0

01

-0.0

01

-0.0

01

0.0

02

0.0

02

0.0

01

exp

lora

tion

acqu

isit

ion

ofn

ewre

serv

esP

ITE

C-

--

--

0.0

02

0.0

01

0.0

01

5.

Copyri

ght

&In

info

rmat

ion

-sec

tor

EIT

--

0.0

18

-Under

construction(INE)

licen

secost

sin

du

stri

es(p

aten

tor

lice

nse

)P

ITE

C-

-6.

Oth

er

pro

du

ct

R&

Din

fin

ance

&E

IT-

-(0

.040,

-(0

.059,

(0.0

68,

(0.0

55,

0.0

27

develo

pm

ent,

oth

erse

rvic

es(d

)0.0

42)

0.0

62)

0.0

72)

0.0

59)

desi

gn

an

dP

ITE

C-

--

--

0.0

52

0.0

36

0.0

58

rese

arc

h+

Res

tE

IT-

-(0

.046,

-(0

.098,

(0.1

61,

(0.0

94

0.1

07

exp

en

ses

ofS

ervic

es0.0

48)

0.1

01)

0.1

65)

0.0

97)

+C

onst

ruct

ion

EIT

--

0.0

13

-0.0

06

0.0

20

0.0

34

0.0

23

PIT

EC

--

--

-0.0

34

0.0

20

0.0

43

34

Tab

le6:

Eco

nom

icC

omp

eten

cies

(Lev

els)

Data

avail

ab

ilit

yan

dest

imate

dsi

ze

of

bu

sin

ess

spen

din

gon

inta

ngib

les,

by

typ

eof

ass

et

(billionsofcurren


tUS$)

Typ

eof

ass

et

Data

avail

ab

ilit

yE

stim

ate

dsi

ze

or

spen

din

gan

dd

ata

sou

rces

1998

1999

2000

US

98-0

02001

2002

2003

2004

2005

Econ

om

icB

ran

dn

ames

&-

-16.4

30

(525,

15.7

01

15.1

32

15.6

15

17.3

32

19.0

85

com

pete

ncie

skn

owle

dge

in785)

firm

-sp

ecifi

chu

man

and

stru

ctu

ral

reso

urc

es

7.

Bra

nd

equ

ity

Ad

vert

isin

gex

pen

dit

ure

sIn

fo4.3

35

5.2

23

5.7

88

217

5.4

68

5.4

11

5.5

73

6.1

53

6.6

45

and

mar

ket

rese

arch

Ad

exfo

rth

ed

evel

opm

ent

ofb

ran

ds

and

trad

emar

ks

EA

S1.7

48

-2.4

16

(9,

2.1

31

1.9

09

1.8

56

2.1

05

2.4

33

28)

Market

preparationfor

IS0.113

-0.588

-0.747

0.291

0.321

0.794

product

innovations

PIT

EC

--

--

-0.2

12

-0.6

32

8.

Fir

m-s

pecifi

cC

osts

ofd

evel

opin

ghu

man

cap

ital

wor

kfo

rce

skil

lsD

irec

tfi

rmex

pen

ses

ET

CL

,0.5

54

-0.6

12

22

0.5

13

0.4

88

0.5

72

0.6

08

0.7

10

EP

A,

EC

VT

Wag

eco

sts

ofem

plo

yee

ET

CL

,4.2

19

-4.9

16

94

4.9

60

4.4

02

4.5

54

5.4

37

5.7

45

tim

ein

trai

nin

gE

PA

,E

CV

T9.

Org

an

izati

on

al

Cos

tsof

orga

niz

atio

nal

EA

S-

2.2

76

2.6

98

81

2.6

30

2.9

23

3.0

59

3.0

29

3.5

52

stru

ctu

rech

ange

and

dev

elop

men

tV

alu

eof

exec

uti

ve

tim

e210

spen

ton

orga

niz

atio

nal

inn

ovat

ion

Gra

nd

tota

l-

-(2

2.0

82,

(1005,

(22.0

96,

(22.3

49,

(25.0

19,

(26.7

93,

29.3

34

--

22.1

10)

1465)

22.1

33)

22.3

96)

25.0

76)

26.8

51)

35

Tab

le7:

Eco

nom

icC

omp

eten

cies

(Rat

ios

over

GD

P)

Data

avail

ab

ilit

yan

dest

imate

dsi

ze

of

bu

sin

ess

spen

din

gon

inta

ngib

les,

by

typ

eof

ass

et

(In%

ofGDP)

Typ

eof

ass

et

Data

avail

ab

ilit

yE

stim

ate

dsi

ze

orspen

ding

an

dd

ata

sou

rces

1998

1999

2000

US

98-0

02001

2002

2003

2004

2005

Econ

om

icB

ran

dn

ames

&-

-2.6

07

(5.6

37,

2.3

07

2.0

75

1.9

95

2.0

63

2.1

08

com

pete

ncie

skn

owle

dge

in8.4

29)

firm

-sp

ecifi

chu

man

and

stru

ctu

ral

reso

urc

es

7.

Bra

nd

equ

ity

Ad

vert

isin

gex

pen

dit

ure

sIn

fo0.8

04

0.9

01

0.9

18

0.8

03

0.7

42

0.7

12

0.7

32

0.7

34

and

mar

ket

rese

arch

Ad

exfo

rth

ed

evel

opm

ent

ofb

ran

ds

and

trad

emar

ks

EA

S0.3

24

-0.3

83

0.3

13

0.2

62

0.2

37

0.2

51

0.2

69

Market

preparation

IS0.0

21

-0.0

93

-0.1

02

0.0

37

0.0

38

0.0

88

forproduct

innovations

PIT

EC

--

--

-0.0

27

--

8.

Fir

m-s

pecifi

cC

osts

ofd

evel

opin

g0.0

00

0.0

00

0.0

00

0.0

00

0.0

00

0.0

00

0.0

00

0.0

00

hu

man

cap

ital

wor

kfo

rce

skil

lsD

irec

tfi

rmex

pen

ses

ET

CL

,0.1

03

-0.0

97

0.0

75

0.0

67

0.0

73

0.0

72

0.0

78

EP

A,

EC

VT

Wag

eco

sts

ofem

plo

yee

ET

CL

,0.7

82

-0.7

80

0.7

29

0.6

04

0.5

82

0.6

47

0.6

34

tim

ein

trai

nin

gE

PA

,E

CV

TE

CV

T9.

Org

an

izati

on

al

Cos

tsof

orga

niz

atio

nal

EA

S-

0.3

93

0.4

28

0.3

86

0.4

01

0.3

91

0.3

61

0.3

92

stru

ctu

rech

ange

and

dev

elop

men

tV

alu

eof

exec

uti

ve

tim

esp

ent

onor

gan

izat

ional

inn

ovat

ion

Perc

ent

of

GD

P(3

.504,

(11,

(3.2

46,

(3.0

65,

(3.1

97,

(3.1

89,

3.2

40

3.5

08)

16)

3.2

52)

3.0

71)

3.2

05)

3.1

96)

36

Tab

le8:

Reg

ress

ions

ofM

arke

tV

alues

onA

sset

Quan

titi

es,

1992

-200

5

OLS

FE

IV-F

E(1

)(2

)(3

)(4

)(5

)la

gged

OA

Tan

gib

leass

ets

1.20

7**

0.88

5**

0.74

3**

0.55

4**

0.110

0.1

35**

(0.0

27)

(0.0

30)

(0.0

19)

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41

Innovation, Tangible and Intangible Investments and the Value of Spanish Firms

Documents

Transcript of Innovation, Tangible and Intangible Investments and the Value of Spanish Firms