Intangible Assets and Firm-Level Productivity Growth in ... · The purpose of this study is to...

Working paper No. 10

May 2015

ISSN 2385-2275

Working papers of the

Department of Economics

University of Perugia (IT)

Intangible Assets and

Firm-Level Productivity

Growth in the U.S. and

Japan

Miho Takizawa

INTANGIBLE ASSETS AND FIRM-LEVEL PRODUCTIVITY GROWTH1

IN THE U.S. AND JAPAN

Miho Takizawa2

Toyo University

Abstract

The purpose of this study is to measure the effect of intangibles on the growth of

developed countries, particularly, at firm level. This paper analyzes the role ”intangibles” play

in firms' growth and performance, in addition to the production factors of labor and tangible

capital, using their financial data in the U.S. and Japan. And this study attempts to analyze

whether companies accumulating intangible assets respond better to shocks (for example,

financial crises) than those without intangible capital.

We could see that intangibles were important sources of productivity growth at the

micro level in the U.S. Those results were not obtained in Japan. The cross term between

intangibles and tangibles was positive and significant in both the U.S. and Japan. This suggests

that if a firm invests more not only in intangibles but also in tangibles, the firm can enjoy a high

productivity growth.

Finally, this paper analyzed whether companies that had invested in intangibles

responded better to shocks than those without intangible capital. The results showed that the

firms with greater intangible capital managed to overcome the crisis in the U.S.

Keywords: Intangible assets, productivity

JEL Classification: J24, O40

1 The author would like to thank Dale Jorgenson, Susan Pharr, Shinju Fujihira, Walter Hatch and participants in the

seminar of Harvard University’s Program on U.S.-Japan Relations for helpful comments and suggestions. I also thank Elizabeth Scanland for her substantial help with proofreading. Financial support from Nomura foundation is gratefully acknowledged. This work was supported by JSPS KAKENHI Grant-in-Aid for Young Scientists (B) 24730252. All errors and omissions are my own responsibility. 2

Faculty of Economics, Toyo University. 5-28-20, Hakusan, Bunkyo-ku, Tokyo, 112-8606, Japan. Email. [email protected] Tel. +81-3-3945-7423 Fax. +81-3945-7667.

1

1. Introduction

In recent years, the impact of the accumulation of intangible assets on a firm’s

economic growth and performance has received a great deal of attention in the field of

economics. The Organization for Economic Cooperation and Development (OECD) now

focuses on intangible assets as new sources of economic growth and has started a project to

improve the measurement of intangible assets both at the macro and firm level.3

What are intangible assets? OECD defines intangible assets as follows. “Intangible

assets are assets that do not have a physical embodiment. Intangible assets have also been

referred to as knowledge assets or intellectual capital.” Corrado, Hulten and Sichel (CHS)

(2006), which is one of the most well-known papers in this field, grouped intangible assets into

three types. The first is computerized information, which includes computer software and

computerized databases. The second is innovative property, which includes research and

development (R&D), copyright, license and design. The last is economic competencies, which

includes advertising, marketing, firm-specific human capital, and organizational capital that

increases firm’s efficiency. Table 1 shows the classification of intangible assets by CHS (2006).

Why do we emphasize intangibles in recent years? One of the answers is that intangible

investment and the accumulation of intangible assets are strongly related to productivity. In

3 The OECD project on intangible assets began at the start of 2011 and concluded at the end of 2012.

2

2012, OECD reported that unmeasured intangible capital accounted for 18% of the growth in

total factor productivity (TFP) in the United States between the mid-1990s and early 2000s.

It is clear that productivity is the key driver of long-term economic growth. In

developed countries such as the U.S. and Japan, that have large amounts of tangible capital

(such as buildings and machines), and where the birthrate has declined and society has aged,

factor-input-type economic growth driven by increasing population and accumulation of

tangible capital cannot be expected in the future. In fact, in many countries, there is evidence

that investment in intangible assets is growing faster than in that of tangible assets. 4 Cleary,

the importance of intangibles in economic things has been increasing.

The purpose of this study is to measure the effect of intangibles on the growth of

developed countries, particularly, at firm level. This paper analyzes the role ”intangibles” play

in firms' growth and performance, in addition to the production factors of labor and tangible

capital, using their financial data in the U.S. and Japan. And this study attempts to analyze

whether companies accumulating intangible assets respond better to shocks (for example,

financial crises) than those without intangible capital.

The major results obtained through this study are as follows:

At the macro level, we found that the intangible investment-GDP ratio in the U.S. was

4 See OECD (2012).

3

highest of all countries in this study. On the other hand, the tangible investment-GDP ratio was

highest in Japan. The U.S. invested more in intangibles than it did in tangibles. The results of

labor productivity decomposition showed that the Japanese capital deepening rate of

intangibles was the lowest of all countries. The share of capital deepening of intangible assets

in the labor productivity growth rate was also lowest in Japan. However, the capital deepening

rate of intangibles was highest in the U.S.

At the micro level, we could see that intangibles were important sources of productivity

growth at the micro level in the U.S. Those results were not obtained in Japan. The cross term

between intangibles and tangibles was positive and significant in both the U.S. and Japan. This

suggests that if a firm invests more not only in intangibles but also in tangibles, the firm can

enjoy a high productivity growth. The firms with greater intangible capital managed to

overcome the crisis in the U.S.

This paper is organized as follows. Section 2 reviews previous studies about measuring

intangible assets in firm level. Section 3 shows the data descriptions. Section 4 provides the

estimation results. Finally, Section 5 concludes the paper.

2. Literature review

One of the most influential papers in this field is Corrado, Hulten and Sichel (CHS)

4

(2006).5 As mentioned above, CHS (2006) grouped intangibles into three types: computerized

information, innovative property and economic competencies. Table 1 shows the classification

of intangible assets by CHS (2006). They measured each category of intangibles at the macro

level in the U.S. and found that the inclusion of intangibles in the study showed a large impact

on economic growth. They also found that the inclusion of intangible investment in the real

output of the nonfarm business sector increases the estimated growth rate of output per hour

by 10 to 20 percent, relative to the case which completely ignores intangibles. Thus, they

showed that the inclusion of intangibles matters for labor productivity growth rates.

Corrado, Haskel, Iommi and Lasinio (CHIL) (2012) extended CHS’s (2006) work. CHIL

(2012) produced a measure by which the EU 27 countries can estimate intangible investment

and calculate growth accounts. The paper shows that the contribution of intangible capital in

some large European countries (e.g., Germany, Italy and Spain) is lower than in the UK and the

U.S.

There are two representative projects that studied intangible assets, COINVEST and

INNODRIVE. COINVEST, which was funded by the European Commission, looked at

investment in intangibles in Europe. 6 The aim of the INNODORIVE project was to improve

5 Kim (2007) provides a survey of topics related to intangible assets. Kim (2007) shows that one of the earliest

works on the macroeconomic measurement of intangibles is a 1987 internal OECD memo by Kaplan. 6 See: http://www.coinvest.org.uk/bin/view/COINVEST

5

the understanding of intangible study by providing new data on intangible capital and new

evidence of its contributions to economic growth in the EU. 7 In Japan, The Japan Industrial

Productivity (JIP) database provides time series of intangibles by industry.8

As mentioned above, a number of studies on intangibles at macro and industry level has

been accumulated in these days. On the other hand, there are few studies at firm level,

because it is difficult to collect sufficient data related to intangibles at this level. However, in

recent years, efforts have been made to construct a quantitative assessment (visualization) of

intangibles at firm level. In Hulten and Hao (2008), one of the most famous studies about

intangible assets in firms, they estimate intangibles (especially R&D and organizational capital)

of U.S. firms, using financial data. They found that hard-to-value assets like intangibles are

usually excluded from firms’ financial data and that excluded assets account for some 40 to 50

percent of the 2006 market value of R&D-intensive companies (e.g., companies in the

pharmaceutical industry) in the U.S. and appear significant in explaining the

market-to-book-value puzzle.

Hulten, Hao and Jaeger (2010) applies the method of Hulten and Hao (2008) to a sample

of firms in Germany and Switzerland firms and compares results with the U.S. They show that

capitalized R&D and organizational capital have a large impact on income statements and 7 See: http://innodrive.org/

8 See: http://www.rieti.go.jp/jp/database/JIP2011/index.html#04-6

6

balance sheets in both countries. And their results show that intangibles appear to be more

important in U.S. business, but the globally competitive companies in both countries are more

“intangible intensive” than the average company.

Following to Hulten and Hao (2008), Takizawa (2013) measures two intangible assets,

R&D stock and organizational capital, and investigates their effect on firms' value. It turns out

that in Japan, firms' accumulation of intangible assets positively influences their value.

Moreover, Takizawa (2013) estimates the investment function that makes Tobin's q an

explanatory variable including intangible assets. As a result, the coefficient of Tobin's q is

positive and significant. This implies that taking into account intangible assets is important in

modeling capital investment action.

These studies recognized that they have relied on strong assumptions in measuring

intangibles from firms’ financial data. For example, they assume that the percentage of the

cost of organizational capital in Selling and General Administration (S&GA) costs is 30 percent

in all firms. While their results are clearly provisional, they say that they are also mindful of the

famous dictum of John Maynard Keynes that it is better to be imprecisely right than precisely

wrong. This paper basically supports his philosophy and tries to estimate intangibles at firm

level, using financial data.

7

3. Data Description

3.1. At macro level

As mentioned in section 2, CHIL (2012) measures intangible investment at the macro

level in the U.S. and the EU-15 countries. Miyagawa and Hisa (2013) measures intangible

investment in Japan. Table 2 shows Miyagawa and Hisa’s (2013) method of measuring

intangibles at the macro level in Japan.9

Figure 1 shows the intangible and tangible investment by the market sector in the U.S.,

the EU-15 area and Japan from 2005 to 2009. The intangible investment-GDP ratio in the U.S.

is the highest of the three. Japan shows a higher propensity to invest in intangibles than does

the EU-15. However, the tangible investment-GDP ratio is highest in Japan. And the difference

between the tangible investment-GDP ratio and the intangible investment-GDP ratio is the

largest in Japan. The U.S. invests more in intangibles than it does in tangibles.

Figures 2 and 3 show the tangible-intangible capital ratio in the U.S. and Japan. The

tangible-intangible ratio in the U.S. is about 4.5 on average. Japan’s tangible-intangible ratio is

about 11.4; that is, Japan has more tangible assets than the U.S. The tangible-intangible ratios

have been decreasing both in the U.S. and Japan. It is clear that intangibles are becoming more

important and economies are becoming knowledge based.

9 As for the method of measuring intangibles at the macro level in the U.S. and EU countries, see pages 43 to 56 in

CHIL (2012).

8

Figures 4 and 5 show the estimated value of intangibles and the percentage of total

intangible capital in the U.S. Figures 6 and 7 show those of Japan. Intangible capital has

increased dramatically in the U.S., and the total amount of intangible capital was $5 trillion in

2010. The 60 percent of total intangible capital is innovative property, including R&D.

Economic competency, including marketing and human capital, is 30 percent, and

computerized information is 10 percent of total intangible capital in the U.S. However, in Japan,

the growth rate of intangible capital has been decreasing. Innovative property occupies the

main portion of intangible capital, and the share of economic competency has been

decreasing.

CHIL (2012) and Miyagawa and Hisa (2013) also analyze sources of growth. In particular,

they decompose labor productivity growth into four items; tangible capital deepening,

intangible capital deepening, change in labor composition and TFP growth.

Oliner and Sichel (2002) shows how to decompose the growth in aggregate labor

productivity at the macro level as follows.10 Z denotes the growth rate of any variable Z. Then,

the growth of output per hour (labor productivity) at the macro level can be written as:

(1) TFPqHKHKHY L

I

K

IT

K

T

where Y denotes output in real terms in market economies; H denotes hours worked in market

10

See the details in appendix 1 in Oliner and Sichel (2002).

9

economies; T and K denote the services provided by the stocks of tangible capital and

intangible capital; and q denotes labor quality. The α terms are income shares; under the

assumptions of Oliner and Sichel (2002), the income share for each input equals its output

elasticity, and the shares sum to one.

Equation 1 shows that growth in labor productivity reflects capital deepening,

improvements in labor quality, and gains in TFP, with the overall growth contribution from

capital deepening constructed as the sum of the contributions from the two types of capital,

tangibles and intangibles. Each such contribution equals the increase in that type of capital per

work hour, weighted by the income share for that capital.

Table 3 shows the results of labor productivity decomposition in the U.S., Japan and EU

countries. First, we can see that capital deepening has become the dominant source of growth

in all countries, if intangibles have been capitalized. But the Japanese capital deepening rate of

intangibles is 0.2%. This rate is the lowest of all countries. The share of capital deepening of

intangible assets in the labor productivity growth rate is also lowest in Japan. On the other

hand, the capital deepening rate of intangibles is highest in the U.S.

Second, the contribution of intangible capital in European countries is lower than in the

U.S. Third, it is clear that TFP growth rate is important for labor productivity growth in all

countries.

10

It seems that the driving force in productivity growth in Japan is the change in labor

composition in Table 3. Miyagawa and Hisa (2013) interprets this result as follows. The

accumulation of intangible assets has played a key role in productivity growth in developed

countries. On the other hand, labor productivity growth in Japan has been attributed to a

compositional shift in the labor market: an increase in high quality labor due to the popularization

of higher education. However, there is a limit to this trend and once the number of people pursuing

higher education hits a ceiling, this compositional effect will be muted. In this sense, it is necessary

to accumulate intangible assets up to a level comparable with other developed countries.

Why is the Japanese capital deepening rate of intangibles low? One of the reasons is

that the accumulation of organizational capital and human capital has slowed since firms have

cut back on educational training costs because of the prolonged depression. The slowdown of

intangible capital accumulation may cause the productivity slowdown in Japan, which means

the loss of international competitiveness.

CHIL (2012) also points out that there is a positive correlation between TFP growth and

the contribution of intangible capital deepening in cross-country analysis. Figure 8 shows a

positive association between TFP (MFP) growth and the contribution of intangible capital

deepening. Figure 9 also shows a positive association between TFP (MFP) growth and the

contribution of tangible capital deepening. The difference between the two figures is the slope

11

of their lines. They show that the positive correlation between TFP (MFP) growth and

intangibles is stronger than that of tangibles. This result shows that the investment in

intangibles may promote technical progress.

3.2. At firm level

To measure intangibles at firm level, this study uses the S&P Capital IQ database,

provided by S&P Capital IQ, which is a multinational financial information provider and a

division of Standard & Poor's. The S&P Capital IQ database includes the financial information

(e.g., balance sheet, profit and loss statement, cash-flow statement) of over three million

private companies, almost 50,000 listed companies, and over 730,000 companies all over the

world.

This paper uses R&D expenditures of each firm to construct the investment data for

innovative property, which is one of the categories of intangible assets. As for economic

competencies, expenditures for Selling and General Administration (S&GA) are used for

measuring investments in organizational capital and human capital. This paper assumes that

30 percent of S&GA expenditures is on investments in organizational capital and human capital,

following CHS(2006). And firms’ expenditures for selling and marketing are used as

investments in market research and advertising. This paper cannot measure investments in

12

computerized information of each firm at this time, because the data for firms’ use of software

and databases isn’t available from balance sheets (B/S) and profit and loss statements (P/L).

This is a topic for future research.

In regressions, labor productivity growth rate is used as a dependent variable. Labor

productivity is measured as follows:

Labor productivity = Y / L

with Y = sales

L = the number of employees.

Dependent variables are as follows:

Intangible investments per total assets = INTANGIBLES / ASSETS

with INTANGIBLES = R&D investments + Investments in organizational capital and

Human capital + Investments in marketing

ASSETS = total assets

Tangible investments per total assets = TANGIBLES / ASSETS

with TANGIBLES = Net property, plant and equipment (t) － Net property, plant

and equipment (t-1)

Total assets = ASSETS

Industry dummy and year dummy

13

As control variables, this paper uses industry dummies and year dummies. The Global

Industry Classification Standard (GICS) is used when I classify industries.

Table 4 shows descriptive statistics for these variables in both the U. S. and Japan.

3.3. The comparison between macro and micro

This subsection shows the intangible data comparison between macro and micro in

both the U. S. and Japan. The data for intangibles at the macro level in the U.S. was obtained

from CHIL (2012). That of Japan was obtained from Miyagawa and Hisa (2013). The method of

measuring intangibles at the micro level is explained in subsection 3.2.

Figures 10 and 11 show the growth rate of intangible investments in the U.S., and

figures 12 and 13 show that of Japan.11 We can see that the growth rate of intangible

investments dramatically declined after the financial crisis in 2008 in both countries. The

growth rate of investment in computerized information was high in the IT bubble period. After

the collapse of the IT bubble, that rate decreased in both the U.S. and Japan. Altogether, the

growth rate of intangible investments in the U.S. is higher than that of Japan as stated in

subsection 3.1.

Figures 14 and 15 show the growth rates of intangible investments calculated by using

11

The values in these tables show relative changes from last year.

14

micro data in the U.S. and Japan. The values in these tables show the median growth rate of

intangible investments in each firm. It seems that the movement of the growth rate at the

micro level is quite similar to that of the macro level in both countries. It can be said that the

data at the micro level, which this paper calculated, can capture the full economic picture for

intangibles.

4. Estimation

4.1 Regression analyses

This paper uses the following equation to analyze the impact of intangibles on labor

productivity growth, using Ordinary Least Squares (OLS).

(2) 1,

1,

1,

1,

1,

,

,ln*ln*ln*.ln

ti

ti

ti

ti

ti

ti

tiASSETS

ASSETS

TANGIBLES

ASSETS

SINTANGIBLEConst

L

Y

ituYeardummymmyIndustrydu **

is a difference operator. ti

ti

L

Y

,

,ln means labor productivity growth of firm i and time

t. .Const means a constant term. itu is an error term. Lagged independent variables (past

variables) are used in equation (2), but not in the industry dummy and year dummy section,

15

because this paper assumes that these variables can influence another with time lags. If X is an

independent variable, 1, tiX is the value of the variable in period t-1. This paper uses

1, tiX ,

2, tiX ,3, tiX in regressions.

The question we have to consider here is whether intangible investments affect labor

productivity growth, as well as tangible investments. We can answer this question by checking

the coefficient of intangibles. Intangibles and tangibles are normalized by assets due to the

adjustment for firm size. Equation (2) also includes a log of assets to control for firm size.

Tables 5 and 6 show the results of regressions in both the U.S. and Japan. A glance at

Table 5 will reveal that labor productivity growth is higher if the firm has a high tangible-asset

ratio. It is natural that the coefficients of assets are negative and significant in all Tables in the

U.S. Diminishing returns to scale can explain the negative relation between size and

productivity growth. As for intangibles, the coefficients of intangibles in manufacturing

industries are all positive and significant. And the coefficients are positive and significant in all

industries using the lagged independent variables, 3, tiX . These results mean that intangibles

are important sources of productivity growth at the micro level in the U.S.

If we look at Table 6, we will see that the coefficients of intangibles are negative or

insignificant in Japan. On the other hand, the coefficients of tangibles are significant in the

regressions with the lagged independent variables, 1, tiX , in all industries and manufacturing.

16

This means that tangibles have a positive influence on labor productivity growth. The

coefficients of assets are insignificant, that is; the relation between size and productivity

growth isn’t observed in these regressions in Japan.

In equation (3) below, a cross term between intangibles and tangibles is added as an

independent variable in the regression, due to check the complementarity between intangible

investments and tangible investments.

(3) 1,

1,

1,

1,

1,

,

,ln*ln*ln*.ln

ti

ti

ti

ti

ti

ti

tiASSETS

ASSETS

TANGIBLES

ASSETS

SINTANGIBLEConst

L

Y

it

ti

ti

ti

tiuYeardummymmyIndustrydu

ASSETS

TANGIBLES

ASSETS

SINTANGIBLE

**ln*ln*

1,

1,

1,

1,

The estimation results are shown in Tables 7 and 8. Table 7 shows the results in the U.S.

Except in the cross term, almost the same results are obtained as former estimations.

Intangible investments have a positive effect on labor productivity growth. If we focus on the

coefficients of the cross term, we see that the coefficients are positive and significant, except

the regressions with the lagged independent variables, 3, tiX . This suggests that if a firm

invested more not only in intangibles (for example, software, R&D, organizational capital, etc.)

but also tangibles (for example, building, machinery, equipment, etc.), the firm could enjoy a

17

high productivity growth. This result proves my hypothesis the connection between intangibles

and tangibles is important for labor productivity growth.

Table 8 shows the estimation results in Japan. There is little change in the results in all

industries. As for the cross term, we can see that the coefficients are positive and significant in

all industries and non-manufacturing industries, as in the U.S. results. From this, it can be

assumed that investments in both intangibles and tangibles are crucial to improve labor

productivity, especially in manufacturing industries in Japan.

4.2 Two-mean comparison tests

As noted in the introduction, one of the purposes of this study is to analyze whether

companies investing in intangibles respond better to shocks than those without intangible

capital. For this purpose, this paper compares two-group mean values of the labor productivity

growth before and after the financial crisis in 2008 using a two-group mean test.

Tables 9 and 10 show the results in the U.S. and Japan. The “Intangible Large” group

consists of firms whose intangible-asset ratios are higher than that of the industry median. The

“Intangible Small” group consists of firms whose intangible-asset ratios are lower than that of

the industry median.

We can see that only in the U.S., the mean value of labor productivity growth in the

18

intangible large group is significantly higher than that of in the intangible small group. That is,

the firms that invested intangibles were stronger throughout the crisis in the U.S. It seems that

during the crisis, those firms could work flexibly using their intangibles. In Japan, however, the

results are not significant.

There are some possible reasons why the effects of intangibles are small in Japan. One

of the possible reasons is as follows. It is often said that “a company is its people.” But

investment in firm-specific human capital in economic competencies has been decreasing

since the collapse of Japan’s bubble economy. In fact, from figure 6, we can see that the

estimated value of economic competencies has been decreasing. Japan may have to increase

investments not only in computerized information or R&D, but also in economic competencies,

especially in human capital, to increase productivity growth.

5. Conclusion

This paper showed the results of CHIL (2012) and Miyagawa and Hisa (2013) at the

macro level and summarized how to measure intangibles. Then, we could see that the

intangible investment-GDP ratio in the U.S. was highest. On the other hand, the tangible

investment-GDP ratio was highest in Japan. And the difference between the tangible

investment-GDP ratio and the intangible investment-GDP ratio was largest in Japan. The U.S.

invested more in intangibles than it did in tangibles.

19

The results of labor productivity decomposition showed that the Japanese capital

deepening rate of intangibles was the lowest of all countries. The share of capital deepening of

intangible assets in the labor productivity growth rate was also lowest in Japan. On the other

hand, the capital deepening rate of intangibles was highest in the U.S.

Next, this paper measured intangibles at firm level using the S&P Capital IQ database,

and ran regression analyses. As a result, we could see that intangibles were important sources

of productivity growth at the micro level in the U.S. Those results were not obtained in Japan.

The cross term between intangibles and tangibles was positive and significant in both the U.S.

and Japan. This suggests that if a firm invests more not only in intangibles but also in tangibles,

the firm can enjoy a high productivity growth.

Finally, this paper analyzed whether companies that had invested in intangibles

responded better to shocks than those without intangible capital. The results showed that the

firms with greater intangible capital managed to overcome the crisis in the U.S.

In conclusion, the results of this study clearly show that the effect of intangibles on

economic growth has been increasing in our knowledge-based economy. It is hoped that we

will develop in detail the method of measuring intangibles at the micro level, and shed light on

the cause of the difference in the degree of intangibles’ influence on each country’s

productivity.

Tables

Table 1. Classification of Intangible Assets

Source: Corrado, Hulten and Sichel (2006)

Computerized information

1. Software

2. Databases

Innovative property

3. Mineral exploration

4. R&D (scientific)

5. Entertainment and artistic originals

6. New products/systems in financial services

7. Design and other new products/systems

Economic competencies

8. Brand equity

a. Advertising

b. Market research

9. Firm-specific resources

a. Employer-provided training

b. Organizational structure

1

Table 2. Miyagawa and Hisa’s (2013) Method of Measuring Intangibles in Japan

Estimation method and data sources

Computerlized information

Custom and

packaged software

We use data of custom and package software investment of JIP Database 2011 (JIP asset alassification no. 38).

Own account

software

We estimate the ratio of the system engineers and programmers to total workers by industry using Population

Census . Multiplying this ratio by the number of total workers in JIP Database 2011, we obtain the number of SE and

programmer by industry. The Census data is available for every five years. For other years, we estimate the ratio by

linear interpolation. We multiply the number of estimated workers by the average wage of system engineers and

programmers. We get wage data from Basic Survey on Wage Structure . We do not take account of other

expenditures except labor cost. We used this result as the expenditure for in-house software except the case of the

information service industry.

Innovative Property

Science and

enginnering R&D

We get data of R&D expenditures from Survey of Research and Development . However the survey does not cover

R&D data in most of service sectors before 2000. Using service sectors' expenditures for R&D outsourcing, which is

available at JIP 2011, we extraporate service sectors' R&D expenditures backwards. Because the survey is

conducted on a fiscal-year basis, the values are then converted to a calendar-year basis.

Mineral exploitation Because expenditures of mineral exploitation are allocated to only mining industry, we follow the estimation by

Fukao, et, al (2009). The Mining Industry Handbook and the Establishment and Enterprise Survey provide data on

expenses for mineral exploitation (the total expenses for geological investigation). Combined the above two surveys

with FCFM, we estimate expenditures of mineral exploitation.

Copyright and licence

costs

Intangible investment in copyright and license costs is assumed to consist of the input from the publishing industry

(JIP industry no. 92) and the video picture, sound information, character information production and distribution

industry (JIP industry no. 93) to JIP industries nos. 1-71 and 73-107.

Other product

development, design,

and research

expenses

In the case of outsourcing of design, display, machine design and architectual design, we estimate intangible

investment by using the sales data of these industries in the Survey of Selected Service Industries and the input from

the other services for businesses industry (JIP industry no.88). We calculate the ratio of the sales of these industries

in the Survey of Selected Service Industries to the nominal output of the other services for businesses industry (JIP

industry no.88) of the JIP 2011 Database for each year that the survey was conducted. The survey is conducted every

three years. Then, the ratio for years in which the survey was not conducted is obtained by linear interpolation. The

estimated value of sales is adjusted by using the number of firms taken from the Establishment and Enterprise

Survey because the Survey of Selected Service Industries is a sample survey. In the case of in-house expenditures,

we only estimated in-house designing. We estimate the ratio of the designers to total workers by industry using the

Population Census. Multiplying this ratio by the number of total workers in JIP Database 2011, we get the number of

designers by industry. The Census data is available for every five years. For other years, we estimate the ratio by

linear interpolation. We multiply the number of estimated workers by the average wage of designers. We get wage

data from the Basic Survey on Wage Structure. We do not take account of other expenditures except labor cost. As

for the estimation in product development in financial service, we assume that 8 percent of the compasation of high-

skilled labors (workers graduated from college) in the financial industry (JIP industry no. 69) and the insurance

industry (JIP industry no. 70) can be regarded as expenditures in intangible assets, following Corrado's suggestions.

These expenditures are treated as those in financial sector and insurance industry respectively.

Category

2

Table 2. (cont.)

Source: http://www.rieti.go.jp/jp/database/JIP2011/index.html#04-6

Table 3. Decomposition of Labor Productivity Growth Rate (1995-2007)

Sources: Miyagawa and Hisa (2013), CHIL (2012)

Economic competencies

Brand equity We get the input from the advertising industry (JIP industry no. 85) from JIP Database 2011.

Firm specific human

capital

We estimate the ratio of off-the-job training costs to the total labor costs from the General Survey on Working

Conditions by industry. Multiplying this ratio by the total labor costs in JIP database (2011 version), we get off-the-

job trainig costs expensed by firms by industry. For the opportunity cost of off-the-job training in terms of working

hours lost, we use the results obtained by Ooki (2003). Using micro-data of The Japan Institute for Labour Policy

and Training’s Survey on Personnel Restructuring and Vocational Education/Training Investment in the Age of

Performance-based Wage Systems (Gyoseki-shugi Jidai no Jinji Seiri to Kyoiku/Kunren Toshi ni Kansuru Chosa),

Ooki calculated the average opportunity cost ratio of off-the-job training to direct firm expenses for training in 1998

for the whole business sector. The value was 1.51. We use this value to estimate the opportunity cost.

Organizational

structure

We assume that 9% of the remuneration of executives is counted as intangible investment for organizational

structure, because 9% of the total working time of executives is spent for the organizational reform and the

restructuring of organization accroding to Robinson and Shimizu (2001). We calculate the ratio of the remuneration

of executives to value added using the Financial Statements Statistics of Corporations by Industry published by the

Ministry of Finance. Then, we get the expenditure for the organizational structure by industry by multiplying this

ratio to value added in JIP database (2011 version)

(Unit: %)

Labor

Productivity

Growth Rate

Capital

Deepening Rate

Change in Labor

Composition

TFP Growth

Rate

Tangibles Intangibles

U.S. 2.7 1.7 0.8 0.9 0.2 0.8

Japan 2.1 0.9 0.7 0.2 0.8 0.5

France 1.9 1.0 0.4 0.6 0.4 0.4

Germany 1.7 1.0 0.7 0.3 0.0 0.7

Italy 0.6 0.7 0.5 0.2 0.2 -0.4

U.K. 2.9 1.5 0.8 0.7 0.4 1.1

3

Table 4. Descriptive Statistics at the Micro Level: U.S. and Japan

U.S.

All Industries

Variable Mean Std. Dev. Max Min Obs

Labor Productivity 0.281 0.337 3.212 0.000 47,086

Intangibles/Assets 0.200 0.348 4.281 0.000 47,086

Tangibles/Assets 0.020 0.069 0.451 -0.377 47,086

Assets 1,197 2,425 17,379 0.004 47,086

Manufacturing





Assets 1,091 2,310 17,379 0.004 21,095

Non-Manufacturing





Assets 1,284 2,512 17,371 0.004 25,991

Note: Unit: million dollars

Japan

All Industries


Labor Productivity 66.606 479.166 35,758.050 1.298 5,670



Assets 764,039 7,472,923 186,000,000 104 5,670

Manufacturing





Assets 342,557 1,717,271 32,600,000 244.000 2,878

Non-Manufacturing





Assets 1,198,504 10,500,000 186,000,000 104 2,792

Note: Unit: million yens

4

Table 5. Regression Results (1) for Labor Productivity Growth: U.S.

Note: ***, ** and * show statistical significance at the 1%, 5% and 10% levels.

All Industry Manufacturing Non-Manufacturing

Labor Productivity Growth (t) Labor Productivity Growth (t) Labor Productivity Growth (t)

Lag(t-1)

Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.

Intangibles/Assets -0.002 0.003 0.017 0.005 *** -0.016 0.004 ***

Tangibles/Assets 0.025 0.002 *** 0.027 0.003 *** 0.025 0.002 ***

Assets -0.024 0.001 *** -0.019 0.002 *** -0.028 0.002 ***

Industry Dummy Yes Yes Yes

Year Dummy Yes Yes Yes

Number of obs 47086 21095 25991

F 8.38 5.92 10.69

Prob > F 0 0 0

R-squared 0.0291 0.0234 0.04

Adj R-squared 0.0256 0.0194 0.0363

Root MSE 0.55027 0.59002 0.51467



Lag(t-2)


Intangibles/Assets 0.000 0.003 0.014 0.005 *** -0.010 0.004 ***


Assets -0.014 0.001 *** -0.012 0.002 *** -0.015 0.002 ***



Number of obs 40209 18240 21969

F 4.22 4.05 4.92

Prob > F 0 0 0

R-squared 0.0173 0.0184 0.022

Adj R-squared 0.0132 0.0139 0.0175

Root MSE 0.50158 0.55427 0.45185



Lag(t-3)


Intangibles/Assets 0.006 0.003 * 0.015 0.005 *** -0.002 0.004

Tangibles/Assets 0.005 0.002 *** 0.002 0.003 0.008 0.002 ***

Assets -0.009 0.002 *** -0.005 0.002 ** -0.012 0.002 ***



Number of obs 35144 16151 18993

F 3.11 3.29 3.35

Prob > F 0 0 0

R-squared 0.0146 0.0167 0.0174

Adj R-squared 0.0099 0.0116 0.0122

Root MSE 0.47084 0.50995 0.43369

5

Table 6. Regression Results (1) for Labor Productivity Growth: Japan




Lag(t-1)


Intangibles/Assets -0.006 0.004 -0.013 0.007 * -0.001 0.006

Tangibles/Assets 0.005 0.002 *** 0.006 0.002 ** 0.003 0.003

Assets 0.003 0.002 0.004 0.003 0.002 0.003



Number of obs 5670 2878 2792

F 2.39 3.28 1.67

Prob > F 0 0 0.0001

R-squared 0.0617 0.0877 0.0517

Adj R-squared 0.0358 0.0609 0.0208

Root MSE 0.2074 0.19618 0.21755



Lag(t-2)


Intangibles/Assets -0.006 0.005 -0.009 0.006 -0.004 0.007

Tangibles/Assets 0.000 0.002 0.001 0.002 0.000 0.003

Assets 0.003 0.002 0.004 0.003 0.001 0.004



Number of obs 5524 2848 2676

F 1.6 2.89 0.97

Prob > F 0 0 0.5585

R-squared 0.0422 0.0753 0.0316

Adj R-squared 0.0158 0.0492 -0.001

Root MSE 0.21553 0.18107 0.24658



Lag(t-3)


Intangibles/Assets -0.009 0.005 * 0.003 0.006 -0.018 0.008 **

Tangibles/Assets -0.001 0.002 -0.002 0.002 0.000 0.003

Assets 0.003 0.002 0.002 0.002 0.004 0.004



Number of obs 5707 3076 2631

F 1.79 3.68 0.92

Prob > F 0 0 0.6799

R-squared 0.0459 0.0874 0.0306

Adj R-squared 0.0203 0.0636 -0.0026

Root MSE 0.21571 0.16857 0.26023

Note) ***, ** and * show statistical significance at the 1%, 5% and 10% level.

6

Table 7. Regression Results (2) for Labor Productivity Growth: U.S.




Lag(t-1)


Intangibles/Assets 0.015 0.005 *** 0.031 0.009 *** 0.002 0.006


Intangibles*Tangibles 0.004 0.001 *** 0.004 0.002 * 0.004 0.001 ***

Assets -0.023 0.001 *** -0.018 0.002 *** -0.027 0.002 ***



Number of obs 47086 21095 25991

F 8.44 5.89 10.73

Prob > F 0 0 0

R-squared 0.0295 0.0235 0.0406

Adj R-squared 0.026 0.0195 0.0368

Root MSE 0.55017 0.58999 0.51454



Lag(t-2)


Intangibles/Assets 0.017 0.005 *** 0.042 0.009 *** 0.002 0.006


Intangibles*Tangibles 0.004 0.001 *** 0.008 0.002 *** 0.003 0.001 ***

Assets -0.014 0.001 *** -0.012 0.002 *** -0.014 0.002 ***



Number of obs 40209 18240 21969

F 4.29 4.15 4.94

Prob > F 0 0 0

R-squared 0.0177 0.0191 0.0223

Adj R-squared 0.0136 0.0145 0.0178

Root MSE 0.50148 0.5541 0.45179



Lag(t-3)


Intangibles/Assets 0.006 0.005 0.017 0.009 ** 0.001 0.006

Tangibles/Assets 0.005 0.003 0.003 0.006 0.010 0.005 **

Intangibles*Tangibles 0.000 0.001 0.001 0.002 0.001 0.001

Assets -0.009 0.002 *** -0.005 0.002 ** -0.012 0.002 ***



Number of obs 35144 16151 18993

F 3.09 3.25 3.32

Prob > F 0 0 0

R-squared 0.0146 0.0167 0.0174

Adj R-squared 0.0099 0.0116 0.0122

Root MSE 0.47084 0.50997 0.4337

7

Table 8. Regression Results (2) for Labor Productivity Growth: Japan




Lag(t-1)


Intangibles/Assets 0.005 0.008 -0.011 0.014 0.013 0.010

Tangibles/Assets 0.013 0.005 *** 0.007 0.008 0.013 0.006 **

Intangibles*Tangibles 0.003 0.001 * 0.000 0.003 0.003 0.002 *

Assets 0.003 0.002 0.004 0.003 0.002 0.003



Number of obs 5670 2878 2792

F 2.39 3.24 1.69

Prob > F 0 0 0.0001

R-squared 0.0622 0.0877 0.0528

Adj R-squared 0.0362 0.0606 0.0216

Root MSE 0.20736 0.19622 0.21746



Lag(t-2)


Intangibles/Assets 0.013 0.008 -0.013 0.013 0.022 0.011 *

Tangibles/Assets 0.013 0.005 *** -0.002 0.008 0.018 0.007 ***

Intangibles*Tangibles 0.005 0.002 *** -0.001 0.003 0.006 0.002 ***

Assets 0.002 0.002 0.004 0.003 0.001 0.004



Number of obs 5524 2848 2676

F 1.65 2.85 1.07

Prob > F 0 0 0.3185

R-squared 0.0438 0.0753 0.035

Adj R-squared 0.0173 0.0489 0.0022

Root MSE 0.21537 0.1811 0.24619



Lag(t-3)


Intangibles/Assets -0.009 0.008 0.012 0.011 -0.021 0.012 *

Tangibles/Assets -0.001 0.005 0.004 0.007 -0.002 0.007

Intangibles*Tangibles 0.000 0.002 0.002 0.003 -0.001 0.002

Assets 0.003 0.002 0.002 0.002 0.004 0.004



Number of obs 5707 3076 2631

F 1.78 3.64 0.91

Prob > F 0 0 0.7042

R-squared 0.0459 0.0876 0.0306

Adj R-squared 0.0201 0.0636 -0.0029

Root MSE 0.21573 0.16857 0.26027

8

Table 9. Two-Group Mean Comparison Test: U.S.

Note: All results in this table are significant at the 1% level.

Table 10. Two-Group Mean Comparison Test: Japan

Note: All results in this table are NOT significant.

Intangibles Large Group Intangibles Small Group

All Industries 0.0396 > -0.0461


Manufacturing 0.0576 > -0.0627


Non-Manufacturing 0.0256 > -0.0312

All results in this table are significant at the 1% level.


All Industries -0.2019 -0.1574


Manufacturing -0.1048 -0.1055


Non-Manufacturing -0.3165 -0.2031

All results in this table are NOT significant.

9

Figures

Figure 1. Tangible vs. Intangible GDP Share (Average Value)

Sources: Japan: Miyagawa and Hisa (2013), Other countries: CHIL (2012)

U.S. (1995-2009) Japan (1995-2008) EU-15 (1995-2009)

Tangibles 9.0 20.9 10.6

Intangibles 10.6 9.4 6.6

0.0

5.0

10.0

15.0

20.0

25.0

Un

it:

%

10

Figure 2. Tangible-Intangible Capital Ratio: U.S.

Note: Constant prices, 2005=1

Source: Intan-invest.net

Figure 3. Tangible-Intangible Capital Ratio: Japan


Source: JIP database

3.9

4

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

GDP 2010: $15 trillionTangible Capital 2010: $21 trillionIntangible Capital 2010: $5 trillion

10.2

10.4

10.6

10.8

11

11.2

11.4

11.6

11.8

12

12.2

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

GDP2008: 506 trillion yen

Tangible Capital 2008: 1,464 trillion yen(≒$15trillion)

Intangible Capital 2008: 136 trillion yen(≒$1.4trillion)

11

Figure 4. Estimated Value of Intangibles: U.S.

Figure 5. Percentage of Total Intangible Capital: U.S.



0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Computerized Information Innovative Property Economic Competencies

Unit: Millions of dollars

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010


12

Figure 6. Estimated Value of Intangibles: Japan

Figure 7. Percentage of Total Intangible Capital: Japan



0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008


13

Figure 8. Correlation between Intangibles and TFP (MFP) Growth

Source: Figure 9 in CHIL (2012)

14

Figure 9. Correlation between Tangibles and TFP (MFP) Growth

Source: Figure 10 in CHIL (2012)

15

Figure 10. Growth Rate of Intangible Investment of Each Category in Market Sector: U.S.


Figure 11. Growth Rate of Total Intangible Investments in Market Sector: U.S.


-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

1. Computerized information 2. Innovative property

3. Economic competencies

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

Total 2+3

16

Figure 12. Growth Rate of Intangible Investment of Each Category in Market Sector: Japan


Figure 13. Growth Rate of Total Intangible Investments in Market Sector: Japan


-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

1. Computerized information 2. Innovative property

3. Economic competencies

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

Total 2+3

17

Figure 14. Growth Rate of Intangible Investments Calculated by Using Micro Data

(Median): U.S.

Source: Author's calculation

Figure 15. Growth Rate of Intangible Investments Calculated by Using Micro Data

(Median): Japan

Source: Author's calculation

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.161

99

5

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

median

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

median

18

Bibliography

Arato, Hiroki, and Katsunori Yamada. "Japan's intangible capital and valuation of corporations

in a neoclassical framework." Review of Economic Dynamics 15.4 (2012): 459-478.

Brynjolfsson, Erik and Andrew McAfee. Race against the Machine: How the Digital Revolution is

Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment

and the Economy. : Digital Frontier Press Lexington, MA, 2011.

Corrado, Carol, Jonathan Haskel, Massimiliano Iommi, and Cecilia Jona Lasinio. Intangible

Capital and Growth in Advanced Economies: Measurement and Comparative Results.

Harvard University, 2nd World KLEMS Conference, 2012.

Corrado, Carol, Charles Hulten and Daniel Sichel. "Intangible Capital and Economic Growth."

No. w11948. National Bureau of Economic Research, 2006.

Corrado, Carol, Charles Hulten and Daniel Sichel. "Intangible Capital and US Economic

Growth." Review of Income and Wealth 55.3 (2009): 661-685.

Edmans, Alex. "Does the Stock Market Fully Value Intangibles? Employee Satisfaction and

Equity Prices." Journal of Financial Economics 101.3 (2011): 621-640.

Fukao, Kyoji, Tsutomu Miyagawa, Kentaro Mukai, Yukio Shinoda, and Konomi Tonogi.

"Intangible Investment in Japan: Measurement and Contribution to Economic Growth."

Review of Income and Wealth 55.3 (2009): 717-736.

Griliches, Zvi. "Market Value, R&D, and Patents." Economics letters 7.2 (1981): 183-187.

--- R and D, Education, and Productivity. : Harvard University Press, 2000.

Hall, Bronwyn H., Jacques Mairesse, and Pierre Mohnen. Measuring the Returns to R&D. No.

w15622. National Bureau of Economic Research, 2009.

Hall, Robert E. The stock market and capital accumulation. No. w7180. National Bureau of

Economic Research, 1999.

19

Hosono, Kaoru, and Miho Takizawa. Do Financial Frictions Matter as a Source of Misallocation?

Evidence from Japan. No. 12A-17. Policy Research Institute, MOF, 2012.

Hulten, Charles R., and Xiaohui Hao. What is a Company Really Worth? Intangible Capital and

the" Market to Book Value" Puzzle. No. w14548. National Bureau of Economic Research,

2008.

Hulten, Charles R., Xiaohui Hao and Kirsten Jaeger. Macro versus Micro Comparisons of

Intangible Capital: The Case of Germany and the U.S. CoInvest publications, 2010.

Jorgenson, Dale W. and J. Steven Landefeld. "Implementation of a New Architecture for the

US National Accounts." The American Economic Review 99.2 (2009): 64-68.

Jorgenson, Dale W. "Investing in Productivity Growth." Technology and Economics (1991): 57.

Kaplan, M. C. "Intangible investment: An essay at international comparison."memorandum to

the OECD Industry Committee, OECD, Paris (1987).

Kim, YoungGak. A suvey on Intangible Capital . No. 2007-10. Center for Economic Institutions

Working Paper Series, 2007.

Masayuki, Morikawa. Financial Constraints in Intangible Investments: Evidence from Japanese

firms. No. 12-E-045. RIETI Discussion Paper, 2012.

McGrattan, Ellen R., and Edward C. Prescott. Is the stock market overvalued?. No. w8077.

National Bureau of Economic Research, 2001.

--- Unmeasured Investment and the Puzzling US Boom in the 1990s. No. w13499. National

Bureau of Economic Research, 2007.

Miyagawa, Tsutomu and Shoichi Hisa. "Estimates of Intangible Investment by Industry and

Productivity Growth in Japan." Japanese Economic Review 64.1 (2013): 42-72.

Miyagawa, Tsutomu and YoungGak Kim. "Measuring Organization Capital in Japan: An

Empirical Assessment using Firm-Level Data." Seoul Journal of Economics 21.1(2008):

169-193.

20

Miyagawa, Tsutomu, Miho Takizawa and Kazuma Edamura. Does the Stock Market Evaluate

Intangible Assets? An Empirical Analysis using Data of Listed Firms in Japan. No. 13-E-052.

RIETI Discussion Paper, 2013.

Miyagawa, Tsutomu and Miho Takizawa. The Role of Intangible Assets on the Economic

Performances in Japan and Korea. No. 11-J-018. RIETI Discussion Paper, 2011.

OECD, New sources of growth: intangible assets. http://www.oecd.org/sti/inno/46349020.pdf,

2012.

Sichel, Daniel and Stephen Oliner. Information technology and productivity: where are we now

and where are we going? Board of Governors of the Federal Reserve System FEDS 29,

2002.

O'Mahony, Mary, and Fei Peng. Workforce Training, Intangible Investments and Productivity in

Europe: Evidence from EU KLEMS and the EU LFS. No. 1. SERVICEGAP Discussion Paper,

2010.

O'Mahony, Mary and Marcel P. Timmer. "Output, Input and Productivity Measures at the

Industry Level: The EU KLEMS Database." The Economic Journal 119.538 (2009):

374-403.

O'Mahony, Mary. "Human Capital Formation and Continuous Training: Evidence for EU

Countries." Review of Income and Wealth 58.3 (2012): 531-549.

Piekkola, Hannu. Intangible Capital in EU 27–Drivers of Growth and Location in the EU. Vol. 166,

Proceedings of the University of Vaasa. Reports, 2011.

Takizawa, Miho. Intangible Investment and Firm Value in Japan. No. 13-J-038. RIETI Discussion

Paper, 2013.

http://www.oecd.org/sti/inno/46349020.pdf

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