Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Chapter 2
The Implications of R&D Intensity for Innovation Efficiency
and Firm Performance: Comparing China, Japan, U.S.*

Zhenyu GONG† and Gary H. JEFFERSON‡

Abstract: China’s innovation surge over the past two decades has clearly led to dramaticincreases in innovation activity. The aggregate numbers, however, obscure criticalknowledge regarding the overall returns to the surge in R&D as relates to firm-level
intangible assets, including but not limited to patents and publications. Based on ourprior work, this paper anticipates the possibility of a structural difference in overall
returns as between China’s high R&D intensity firms and low intensity R&D firms. Thisdifference may result from advantages for the more efficient R&D systems for firms
spending relatively less on R&D; alternatively, the high R&D intensity firms may
acquire advantages from scale economies in patent production or value-adding spillovers
for the firm. The paper employs a panel threshold regression model to identify the mostrobust threshold between high and low-intensity R&D firms. As anticipated, we findsignificant differences in the relative efficiency of high and low-intensity firms; as well
as between China, Japan, and the U.S. The differential returns to patents, publications,and other forms of returns to R&D show benefits to resource reallocation across thevarious forms of R&D activity.
Key Words: Tobin’s Q; R&D intensity; threshold model; patents; publications; China;
marginal returns* The authors deeply appreciate the support, comments, and suggestions offered by Dr.
Koichiro Kimura as well as other members of our research group, including Dr. Hiroshi
Matsui, Prof. Kazuyuki Motohashi, Mr. Shun Kaida, and Dr. Janthorn Sinthupundaja.
†

zhenyugong@brandeis.edu‡

jefferson@brandeis.edu

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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

1. Introduction
Globally, the past two decades have witnessed witnessed a surge of innovation activity,
having been most pronounced in Asia, including China and Japan. China, the focus of
this paper, shows the most dramatic increase. Having reestablished its patent law in 1985,
China’s research and development (R&D) activity has since accelerated in relation to thegrowth of GDP, yielding substantial increases in the R&D to GDP ratio. This paper
attempts to identify the types of firms – those high R&D intensity firms versus those with
low R&D intensity – that have employed their R&D resources most efficiently, using
both quantity and quality measures. The study compares the R&D performance of highand low-R&D intensive firms across industries within China; it also compares R&D
intensity and performance in China with that of Japan and the US.
A key finding of our earlier research relating to innovation efficiency in China,
Japan, and the U.S. is that of substantial firm heterogeneity with respect to R&D-patent
efficiency as well as shifting profiles of this heterogeneity over our sample period. In
particular, firms with high patent intensities show differences in their patterns ofinnovation, e.g., higher marginal returns to R&D versus those with little R&D andpatenting. By dividing our sample into high and low R&D intensity firms, this paper
attempts to identify the structural differences in R&D over the relevant sample period.
Specifically, in this paper, we test whether the marginal impact of innovation
activities differs between high R&D intensity and low R&D intensity firms. Using amodeling approach similar to Hall et al. (2005), we investigate the impact of R&D
intensity, patenting, publications, and other innovative activity on Tobin’s Q, the ratio ofa firm’s total market value to the replacement value of its tangible fixed assets. By
inference, the ratio measure the impact of the firm’s R&D investment on the intangible
assets of the firm, i.e., its intellectual property, reputation, and capabilities in the realm oftechnological innovation. Our approach incorporates two innovations. The first resultsfrom the fact that we do not have access to patent citations as Hall et al. do, causing us tomeasure the quality of the firm’s patenting innovation in terms of publication production
rather than citations. The second, critical difference, is that in order to identify the salient
structural differences between firms with high and low R&D intensity, similar to Hansen(1999), we utilize the panel threshold regression method, which is able to identify thethreshold value of firm R&D intensity that provides the best overall fit for the combined
single equation estimate.

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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Using this method, we are able to estimate the marginal products of patenting
intensity, publication intensity, and R&D intensity, controlling for patents andpublications, on Tobin’s Q. Where as Hall et al. use U.S. firm-level data to estimate theirmodel for the period 1963-1995 for the U.S. economy, we use firm-level data spanning2013-2018 for the Chinese economy, which we complement with applications to theJapanese and U.S. economies for comparative purposes. This approach enables us tomake comparisions regarding the relative returns to patenting, publications, and other
R&D contributions as between China, Japan, and the U.S. The approach also reveals
difference in returns within each of the countries. The stricing differences in returns to
R&D activity in China highlight the benefits of reallocating R&D effort across patenting,publications, and other forms of innovation enhancing R&D activity.
We anticipate that the empirical results could show an efficiency advantage ineither direction. The low intensity R&D firms may exhibit greater patent production
efficiency simply because they are more parsimonious in the use of their limited R&D
resources. On the other hand, high R&D intensity firms may enjoy scale and scopeadvantages from a large and wide portfolio of patent production. We also test for the
possibility that R&D spending may result in reputation, supply chains, and otherintangible assets that are not captured solely by patent or publication production.
The history of patenting in China is short, but its impact is far-reaching. China
enacted its first patent law in 1950, but little patenting transpired until 1985 when China
reinstated its patent law. According to statistics published by the recently renamed China
National Intellectual Property Administration, (CNIPA), 1 by the end of 2017, China’s
domestic (excluding Hong Kong, Macao, and Taiwan) invention patent ownership totaled
1.356 million, with 9.8 invention patents per 10,000 population. These figures havepropelled China to first place in the world in terms of the sheer quantity of patentapplications for the past seven consecutive years. As anticpated, the surge in patenting
activity has been accompanied by a surge in R&D spending. While 25 years ago, in 1995,
China’s R&D/GDP ratio was only 0.6% in recent years, it has gown to levels comfortably
in excess of 2%. This level of aggregate R&D intensity compares well with the largest of
the OECD economies which also report levels in the 2-2.5% range.
Our findings will be beneficial for firm strategy and government policy. On the
one hand, through our statistical results, firms can have a more detailed view of the1

From 1985 to 1994, China received 439,529 patent applications, of which 380,431 (86.6 percent)were domestic applications, and 59,098 (13.4 percent) were foreign applications.
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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

relationship between various innovation activities and the market value of firms operatingin their domestic economies. The results have bearing on government policy toward R&D
subsidies and taxation. They also have implications for firm behavior, including the leveland proportion of R&D spending, as well as its distribution over patents, publications,and other innovative activity.
The remainder of the paper is organized as follows: In Section 2, we present theliterature review. Section 3 presents a theoretical context for interpreting the implicationsof R&D intensity. Section 4 describes and summarizes the data. Section 5 presents themodel and method. Section 6 reports the empirical result. In Section 7, we summarize
certain of our conclusions and discuss priorities for further research.

2. Literature Review
The literature on the relationship between R&D intensity and firm performance validates
our research objective. Evidence by Ugur et al. (2015) shows an inverted-U relationshipbetween R&D intensity and firm survival in the UK. When R&D intensity is low, R&D
exhibits a positive marginal effect on firms performance and survival. However, when
the R&D intensity level is too high, it requires too much investment into R&D
expenditure but cannot get enough return to fulfill it, causing the firm's death. Besides,
Kima (2015) and Coccia (2009) both point out an optimal level of R&D expenditureconcerning firm development; at the margin, the returns to research diminish.
A good deal of evidence shows the link between R&D and firm performance andpatenting activity at the firm level. Erickson and Jacobson (1992) show that more patents
enable firms to earn high profits and prevent rivals’ imitation. Wang (2011) also
concludes that firms that invest more in R&D enjoy higher rates of profitability than firmsthat do not. Thus, R&D expenditure and patenting activities have emerged as a criticalfactors in promoting firm competitiveness worldwide.
A number of empirical studies have been devoted to uncovering the possible
underlying relationship between firm performance and specifical innovation activities,
most often but not always patenting. Those relating to China tend to be ambiguous. Somefind a positive relationship (Johnson and Pazderka, 1993; Long and Ravenscraft, 1993;
Lee and Shim, 1995; Monte and Papagni, 2003; Connolly and Hirschey, 2005; Ho et al.,2006; Ghaffar and Khan, 2014), while other notes a negative impact (Gou et al., 2004;
Lin and Chen, 2005; Lin et al., 2006; Artz et al., 2010; Pandit et al., 2011; Donelson and30

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Resutek, 2012). As set forth in the introduction, certain of these ambiguous results may
disguise the diversity of results across firms with varying degrees of R&D intensity; hence
the focus of this paper.
The literature provides a solid foundation for our method. The panel thresholdregression model (PTR) is widely used in finance and development economics topics.
Ibhagui (2009) uses a firm’s size as a threshold variable, investigating whether R&D
expenditure and capital structure affect firms’ ROA and ROE. He finds that thesevariables’ effects have varying magnitudes on firm performance, depending on firm size.
Similarly, Chen and Lee (2017) apply the PTR model, studying the factors influencing
firms’ market value in Asia. They take corporate social responsibility as the thresholdvariable. Firms fall into disparate groups and conclude that CSR investment does notenhance company value until it exceeds the value transition threshold. Further, Ngundu(2017) also uses this model to examine the FDI impact on countries’ GDP growth. He
concludes that groups with different FDI levels determine the efficiency in using
endowments.

3. Theoretical Perspective
Scotchmer (2004) provides a helpful perspetive on the implications of R&D effort. Figure
1 consists of the following variables. That figure includes the following variables:
P: the probability the research fails.
R: the revenue earned by the firm.
S: The total social benefit of the innovation, including the externality, S > R.
nc: Total cost = n × marginal cost.
•

Possible equilibria:

𝑛𝑛𝑐𝑐 : profit maximization for firms, max [(1 − 𝑝𝑝𝑛𝑛 ) 𝑅𝑅 − 𝑛𝑛𝑛𝑛].
𝑛𝑛∗ : profit maximization considering externality, max(1 − 𝑝𝑝𝑛𝑛 ) 𝑆𝑆 – 𝑛𝑛𝑛𝑛.
𝑛𝑛𝑒𝑒 : free-entry level of R&D input, 𝑛𝑛𝑛𝑛 =(1 − 𝑝𝑝𝑛𝑛 ) 𝑅𝑅.

Key features and results of the theoretica perspective include: The increasing probability of a successful research outcome with the addition of
more reseachers;
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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

 Diminishing returns to R&D effort as additional researchers attempting to achievea given invention engage in wasteful duplication; and Free entry, i.e., high R&D effort, results in the highest probability of success
combined with the lowest marginal revenue due to wasteful duplication.
Hence, as shown in Figure 1, for a given invention, the addition of resarchers results in a
increasing probability of success with diminishing returns to additional researchers: MRnc >MRn* > MRne.
Figure 1: Optimal Patent Design; Possible Equilibria

Source: Scotchmer (2004, p. 101)

We deduce from Figure 1 that if the returns to R&D effort for high and low R&D
intensive firms are equal or higher in the high R&D intensity firm, it must be that the highR&D intensity firm is conducting multiple research projects such that there exists limited
overlap or that economies of scope between the individual projects and scale economies
for the overall research operation offset the duplication condition. We intend to map our
results into Figure 1 as a starting point for our analysis.

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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

4. Data Description
Our data set, obtained from Orbis, spans the period 2011 to 2018. All of the included
firms are classified as “large size firms.” We draw from the data set the following
variables: Dependent variable: Tobin’s Q, an indicator of firm’s innovation performance, i.e.,
intangible asset creation
 Independent variables:-

R&D/ASSETS, an indicator of R&D contribution.

-

PAT/R&D, an indicator of patent efficiency.

-

PUBS/PAT, an indicator of patent quality (substitutes for citations used by Hallet al.)-

Stock measures of R&D, PAT and PUBS. We directly get yearly measures ofthe stock of patents from the database, and accumulate yearly data to get R&D
and PUBS.

-

Dummies: year dummy

For the purposes of creating a balanced data set, Table 1 is constructed to show
the number of firms and total number of observations that can be fashed from different
time periods. The panel threshold regression model requires the use of a balanced data
set. On the other hand, as a non-linear model, we are unable to use the fixed effects
estimator. We are, however, able to incorporate fixed effects variables for the year
observations and industry classifications.
Table 1: The Number of Observations Each Year to Achieve a Balanced Dataset,
China
PeriodYears

Number of Obs.

Number of firms

2011-2018

8

4848

606

2013-2018

6

8034

1339

2015-2018

4

5644

1411

Source: The authors.

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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

As shown in the table, the maximum number of observations results from the 6year panel spanning 2013-2018. For this reason, we select this period for our regression
analysis.
Table 2 reports the summary statistics for the key variables. Our measure ofresearch effectiveness is Tobin’s Q, a measure of the ratio of the firm’s total market value
to the book value of its tangible physical assets. Values of Tobin’s Q > 1 indicate that thefirm has acquired intangible assets, such as intellectual property, reputation, and otherintangible factors, many of which are likely to be associated with the firm’s research effort.
Table 2: Summary Statistics of key control variables*
Obs.
Mean
SD
Min**
MedianVariable
China
Tobin’s Q
AssetsR&D/Assets
Patents/R&D
Publications/Patents
Japan
Tobin’s Q
AssetsR&D/Assets
Patents/R&D
Publications/Patents
US
Tobin’s Q
AssetsR&D/Assets
Patents/R&D
Publications/Patents

Max

4274
4274
4274
4274
4274

1.321
3.78e+06
0.020
0.038
0.197

1.010
1.87e+07
0.019
0.125
0.162

0.000
25950
0.000
0.000
0.001

1.065
7.39e+05
0.016
0.014
0.155

4.936
3.93e+08
0.448
3.177
0.842

5585
5585
5585
5585
5585

0.676
4.84e+06
0.025
0.073
0.107

0.595
2.03e+07
0.030
0.091
0.074

0.021
8155
0.000
0.001
0.000

0.497
7.89e+05
0.017
0.047
0.091

4.999
4.74e+08
0.710
0.798
0.444

2504
2504
2504
2504
2504

1.864
1.23e+07
0.110
0.018
0.124

1.127
4.44e+07
0.138
0.028
0.128

0.005
580
0.000
0.000
0.000

1.586
9.91e+05
0.061
0.010
0.086

4.979
6.57e+08
0.965
0.260
0.750

Note: *The table and our analysis has omitted certain observations that are implausible.
Specifically, we limit Tobin’s Q to values ≤ 5. Also for R&D/Assets, we sorted the valuations

through two steps. In order to recenter the mean, we first eliminated observations that exceeded
25x the mean; thereafter we eliminated observations that exceeded 5x the mean. ** For minimum
values that are reported as 0.000, the minimum value is smaller than 0.001, but not zero, i.e., 0 <X < 0.001.
Source: The authors.

Our independent variables include the number of publications each year, R&D
expenditure, total profit, sales, intangible assets, the number of patients each year, thepatent portfolio value each year. The threshold variable, R&D intensity, is calculated by:34

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

𝑅𝑅&𝐷𝐷 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 =𝑅𝑅&𝐷𝐷
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴The number of patents each year is calculated by differencing the number of stock
patents each year. All other variables are directly extracted from the database. Table 2reports the summary statistics for China, Japan and the U.S.
Table 2 shows certain maximum values that appear as outliers compared with themean and median values shown for the same variable. Most notable, and concerning,among these is the maximum measure of R&D intensity, i.e., R&D/Assets, reported as
122.931 for a U.S. firm. This figure substantially exceeds those of 0.451 and 1.233 forthe counterpart Chinese and Japanese firms. Appendix 2 reports levels of R&D intensity
for five of the most R&D intensitive firms in China, Japan, and the U.S. As the Table
shows, the figures for these high-intensity R&D firms are all substantially greater thanthose for certain of the largest and well known firms shown in the Table. Indeed, within
our firm sample the top 50 high intensity R&D firms are all U.S. firms. It appears that themost R&D intensive firms are among the smaller of the large firms in our sample withrespect to their accumulated physical assets. As a robustness test, we drop these outlier
observations, including only those for whom R&D intensity < 1.

5. The Model
The model used for the analysis consists of several parts. The first is for the constructionof the dependent variable, Tobin’s Q, which measures the ratio of the firm’s total market
value to the replacement cost of its physical assets. The second part is the regression
model, which is similar to that employed by Hall et al. (2005). Finally, in order to assessthe difference in R&D performance between high and low R&D intensity forms, weemploy the panel threshold regression model technique, which identifies the thresholdvalue of our R&D intensity measure that results in the most robust estimation result when
we combine estimates of the high and low R&D intensity firms in the same regression
equation. We describe the relevant method for each of these elements.

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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

5.1. The Construction of Tobin’s Q Equation
We use the firm-level market value function developed by Griliches (1981). In this model,
the firm’s total market value (V) is assumed to be the combinated values of its tangible
physical assets (A) and its intangible assets, otherwise characterized known as its
knowledge assets (K) as shown below:
𝑉𝑉𝑖𝑖𝑖𝑖 = 𝑞𝑞𝑡𝑡 (𝜋𝜋𝑡𝑡 𝐴𝐴𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑡𝑡 𝐾𝐾𝑖𝑖𝑡𝑡 )𝜎𝜎(1)

In Eq. (1), 𝜋𝜋𝑡𝑡 and 𝛾𝛾𝑡𝑡 denote the shadow price of physical assets and applied knowledge

assets at time t, respectively. The scale parameter allows for a non-constant scale eﬀect
in the market value function. Finally, q is an intercept representing the “current market

valuation coefficient,” e.g., differential risk and monopoly conditions, which in ourempirical application, we captured by using a year dummy. All the variables are in thenominal terms. Like Griliches (1981), we impose the restriction, 𝜎𝜎 = 1 representing thestandard property of constant returns of scale.

Consistent with Hall et al. (2005) we take logarithms of both sides of Eq. (1), andrecombine as:log(𝑉𝑉𝑖𝑖𝑖𝑖 ) = log(𝑞𝑞𝑡𝑡 ) + log(𝜋𝜋𝑡𝑡 𝐴𝐴𝑖𝑖𝑖𝑖 ) + log(1 + 𝛾𝛾𝑡𝑡 𝐾𝐾𝑖𝑖𝑖𝑖 /𝜋𝜋𝑡𝑡 𝐴𝐴𝑖𝑖𝑖𝑖 )So that 𝜋𝜋𝑡𝑡 𝐴𝐴𝑖𝑖𝑖𝑖 appears in the denominator of 𝛾𝛾𝑡𝑡 𝐾𝐾𝑖𝑖𝑖𝑖 /𝜋𝜋𝑡𝑡 𝐴𝐴𝑖𝑖𝑖𝑖 and as the log of 𝜋𝜋𝑡𝑡 𝐴𝐴𝑖𝑖𝑖𝑖 . In thisformation, 𝛾𝛾𝑡𝑡 /𝜋𝜋𝑡𝑡 denotes the shadow value of knowledge assets relative to the physicalassets of the firm.

Considering the constant returns to scale, 𝜎𝜎 = 1, log A can be removed to the left-

hand side, and the model estimated with the conventional Tobin’s Q as the dependent
variable. Then, we can get the estimating equation,log(𝑄𝑄𝑖𝑖𝑖𝑖 ) = log(𝑉𝑉𝑖𝑖𝑖𝑖 /𝐴𝐴𝑖𝑖𝑖𝑖 ) = log(𝑞𝑞𝑡𝑡 ) + log(1 + 𝜑𝜑𝑡𝑡 𝐾𝐾𝑖𝑖𝑖𝑖 /𝐴𝐴𝑖𝑖𝑖𝑖 ) + 𝜀𝜀𝑖𝑖𝑖𝑖

where q represents the time dummy/fixed effect, such that 𝑄𝑄𝑖𝑖𝑖𝑖 denotes Tobin’s Q, and theintercept of the model can be interpreted as an estimate of the logarithmic average ofTobin’s Q for each year. 𝜑𝜑𝑡𝑡 measures the shadow value of applied knowledge assets
relative to the physical assets that do not account for in the denominator of q. If knowledge
assets make a diﬀerence to market value, then 𝜑𝜑𝑡𝑡 > 0.
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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

The knowledge creation process is treated as a continuum going from R&D topatents, as shown in Hall et al. (2005). R&D reveals the commitment of a firm’s resources
to innovation. Patents catalog the success in generating knowledge that the firm can inprinciple act to appropriate and gain value. As previously explained, rather than use
citations as a measure of patent quality, we use publications under the assumption that
intellectual property that has been vetted in published papers, is, on average, morescientifically and technologically advanced that that which has not been similarly vetted(e.g., Harhoff et al., 1999).
There may be also be spillover benefits that result from R&D, someof which areassociated with specific patents and publications, others of which accumulate from
unpatented or unpublished innovation, such as technology that is appropriated by secrecy
or rapid movement up the technology ladder. Reputation, intellectual and supply chain
links and other intangible assets may accrue from R&D spending.
Accounting for the role of patenting, our basic estimation equation is;log(𝑄𝑄𝑖𝑖𝑖𝑖 ) = log(𝑞𝑞𝑡𝑡 ) + log �1 + 𝛽𝛽1

𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖
𝐴𝐴𝑖𝑖𝑖𝑖

𝑃𝑃𝑃𝑃𝑃𝑃

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃+ 𝛽𝛽2 𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖 + 𝛽𝛽3 𝑃𝑃𝑃𝑃𝑃𝑃 𝑖𝑖𝑖𝑖 � + 𝜀𝜀𝑖𝑖𝑖𝑖
𝑖𝑖𝑖𝑖

𝑖𝑖𝑖𝑖

(2)

Eq. (2) is nearly identical to that used by Hall et al (2005). The only differences is that
we substitute the stock of publications for Hall et al.’s use of citations. The measures of
R&D, PAT, and PUBS, as well as A, physical assets, are all stocks. Therefore, as do Hallet al., we expect that the use of stocks to measure the independent variables whereas V,
the current market value is a flow variable, the stocks should not be highly sensitive totransitory shifts in the firm’s market value. Hence endogeneity issues should be minimal.
5.2. Panel Threshold Regression Model
A persistent theme of this paper has been that the impact on firm performance of a unitof R&D expenditure may vary with firm characteristics, such as R&D intensity. It could
be the the impact of R&D investment is dominated by sharply diminishing marginal
returns, or R&D investment may accumulate surplus even as R&D intensity rises.
To determine whether firms may fall into distinct groupings, we use the dynamic
panel threshold regression model by Hansen (1999). Considering a single threshold model,
𝑦𝑦𝑖𝑖𝑖𝑖 = 𝜇𝜇𝑖𝑖 + 𝛽𝛽1 𝑋𝑋𝑖𝑖𝑖𝑖 𝐼𝐼 (𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 ≤ 𝛾𝛾) + 𝛽𝛽2 𝑋𝑋𝑖𝑖𝑖𝑖 𝐼𝐼 (𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 > 𝛾𝛾) + 𝑒𝑒𝑖𝑖𝑖𝑖
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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

where 𝑦𝑦𝑖𝑖𝑖𝑖 denotes the dependent variable, i denotes the time-invariant fixed eﬀect, is thethreshold and the threshold variable, 𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 . R&D intensity is smaller or larger than thethreshold. The coeﬃcients 𝛽𝛽1 , 𝛽𝛽2 are 1×K vectors, and the regressor 𝑿𝑿𝒊𝒊𝒊𝒊 is also a 1×K

vector.

The observations are divided into disparate groups depending on whether thethreshold variable is smaller or larger than the threshold. We assume that both 𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 and𝑋𝑋𝑖𝑖𝑖𝑖 are time-variant, and the error term 𝜀𝜀𝑖𝑖𝑖𝑖 is independent and i.i.d. with zero mean andfinite variance.

The model can be rewritten as:

where𝑦𝑦𝑖𝑖𝑖𝑖 = 𝜇𝜇𝑖𝑖 + 𝑋𝑋𝑖𝑖𝑖𝑖 (𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 , 𝛾𝛾)𝛽𝛽 + 𝑒𝑒𝑖𝑖𝑖𝑖𝐼𝐼(𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 ≤𝛾𝛾)
𝑋𝑋𝑖𝑖𝑖𝑖 = �𝑋𝑋𝑋𝑋𝑖𝑖𝑖𝑖𝐼𝐼(𝑟𝑟𝑟𝑟𝑟𝑟
�.
>𝛾𝛾)
𝑖𝑖𝑖𝑖

𝑖𝑖𝑖𝑖

Given 𝛾𝛾, the ordinary least-squares estimator of 𝛽𝛽 is

𝛽𝛽̂ = {𝑋𝑋 ∗ (𝛾𝛾)′ 𝑋𝑋 ∗ (𝛾𝛾)}−1 {𝑋𝑋 ∗ (𝛾𝛾)𝑦𝑦 ∗ }where 𝑋𝑋 ∗ (𝛾𝛾) and 𝑦𝑦 ∗ are within-group deviations. The residual sum of squares (RSS) is
given by′
𝑅𝑅𝑅𝑅𝑅𝑅 = 𝑒𝑒�∗ 𝑒𝑒�∗ .

Chan (1993) and Hansen (1999) recommend the estimation of 𝛾𝛾 by least squares. Thismethod is most comfortable to achieve by minimization of the concentrated sum ofsquared errors RSS. Hence the least-squares estimator of 𝛾𝛾 is
𝛾𝛾� = arg min 𝑅𝑅𝑅𝑅𝑅𝑅(𝛾𝛾).
𝛾𝛾Hansen (1999) proved that 𝛾𝛾� is a consistent estimator for 𝛾𝛾.
38Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Further, we can also extend this model into multiple thresholds; for example, thedouble threshold model is𝑦𝑦𝑖𝑖𝑖𝑖 = 𝜇𝜇𝑖𝑖 + 𝛽𝛽1 𝑋𝑋𝑖𝑖𝑖𝑖 𝐼𝐼 (𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 ≤ 𝛾𝛾2 ) + 𝛽𝛽2 𝑋𝑋𝑖𝑖𝑖𝑖 𝐼𝐼(𝛾𝛾2 ≥ 𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 > 𝛾𝛾1 ) + 𝛽𝛽3 𝑋𝑋𝑖𝑖𝑖𝑖 𝐼𝐼(𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 ≤ 𝛾𝛾1 ) + 𝑒𝑒𝑖𝑖𝑖𝑖

where 𝛾𝛾1 and 𝛾𝛾2 are the thresholds that divide the observations into three groups with
coeﬃcients 𝛽𝛽1 , 𝛽𝛽2 , and 𝛽𝛽3 .

Thus, our final estimation equation will be, take single threshold model as an

example,log(𝑄𝑄𝑖𝑖𝑖𝑖 ) = log(𝑞𝑞𝑡𝑡 ) + 𝐹𝐹1 (𝑋𝑋𝑖𝑖𝑖𝑖 )𝐼𝐼(𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 ≤ 𝛾𝛾) + 𝐹𝐹2 (𝑋𝑋𝑖𝑖𝑖𝑖 )𝐼𝐼 (𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 > 𝛾𝛾) + 𝜀𝜀𝑖𝑖𝑖𝑖 .

So that we estimate two values of each of the coefficients, one for the high R&D intensity
firms; the other for the low-inensity R&D firms, (i.e., k = 2), our estimation equation is:

𝐹𝐹𝑘𝑘 (𝑋𝑋𝑖𝑖𝑖𝑖 ) = log �1 + 𝛽𝛽1𝑘𝑘

𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖
𝐴𝐴𝑖𝑖𝑖𝑖

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑈𝑈𝑈𝑈𝑈𝑈+ 𝛽𝛽2𝑘𝑘 𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝑘𝑘 𝑃𝑃𝑃𝑃𝑃𝑃 𝑖𝑖𝑖𝑖 � , 𝑘𝑘 = 1,2.
𝑖𝑖𝑖𝑖

𝑖𝑖𝑖𝑖

Further, to access their marginal impacts in this nonlinear model, we need to compute thesemi-elasticity,
𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 −1
𝜕𝜕 log 𝑄𝑄̂̂̂̂= 𝛽𝛽1𝑘𝑘 �1 + 𝛽𝛽1𝑘𝑘+ 𝛽𝛽2𝑘𝑘+ 𝛽𝛽3𝑘𝑘
� , 𝑘𝑘 = 1, 2
𝜕𝜕 (𝑅𝑅&𝐷𝐷/𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)
𝐴𝐴𝑖𝑖𝑖𝑖
𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖

𝜕𝜕𝜕𝜕
𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 −1= 𝛽𝛽̂1𝑘𝑘 �1 + 𝛽𝛽̂1𝑘𝑘+ 𝛽𝛽̂2𝑘𝑘+ 𝛽𝛽̂3𝑘𝑘� 𝑄𝑄�, 𝑘𝑘 = 1, 2
𝜕𝜕 (𝑅𝑅&𝐷𝐷/𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)
𝐴𝐴𝑖𝑖𝑖𝑖
𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖
And similarly, for Patent/R&D and Publications/Patents:
𝜕𝜕 log 𝑄𝑄
𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 −1̂̂̂̂= 𝛽𝛽2𝑘𝑘 �1 + 𝛽𝛽1𝑘𝑘+ 𝛽𝛽2𝑘𝑘+ 𝛽𝛽3𝑘𝑘
� , 𝑘𝑘 = 1,2
𝜕𝜕 (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃/𝑅𝑅&𝐷𝐷)
𝐴𝐴𝑖𝑖𝑖𝑖
𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖

𝜕𝜕 log 𝑄𝑄
𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 −1= 𝛽𝛽̂3𝑘𝑘 �1 + 𝛽𝛽̂1𝑘𝑘+ 𝛽𝛽̂2𝑘𝑘+ 𝛽𝛽̂3𝑘𝑘
� , 𝑘𝑘𝜕𝜕 (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃/𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃)
𝐴𝐴𝑖𝑖𝑖𝑖
𝑅𝑅&𝐷𝐷𝑖𝑖𝑖𝑖
𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖
= 1,2

39

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

6. Results
6.1. Threshold Analysis
Before we run the regression on our full model, we must identify the threshold values inour model. We test the significance of a single threshold and double threshold model tochoose which model we will use. Table 3 below shows the threshold effect testing using
Hansen’s (1999) method. We can see that the single threshold is significant at the 95%
level, while the double threshold test is not significant even at a 90% level. We also
attached the 95% confidence interval for our single and double threshold model. Hence,
we can conclude for China that the single threshold model is significant, whereas, for the
double threshold model, the first threshold is significant, but the second threshold is not.
Table 3: Test Threshold Effects Using Hansen (1999), China
Test for a single threshold
F statistics
102.63P-value
0.000
Critical values 10% 5% 1%
54.1624, 65.2397, 134.1339
Test for double threshold
F statistics
11.77
P-value
0.900
Critical values 10% 5% 1%
37.4677, 39.5504, 57.1788
Threshold estimator 95%
Single threshold modelThreshold
Th-1
0.135
Double threshold modelThreshold
Th-21
0.135
Th-22
0.361

Lower
0.124

Upper
0.146

Lower
0.124
0.331

Upper
0.146
0.391

Source: The authors.

Applying the same procedure, we obtain the threshold estimations for Japan andthe US. For Japan and the U.S. in Table 4, we find that, as with China, the double
threshold estimates are insignificant and therefore use the single threshold estiamtes for
all three economies.

40

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

China
Japan
US

Table 4. Threshold Estimation (R&D Intensity)
Threshold Estimation
Number of Observations
Mean
SD
Low R&D intensity
High R&D
intensity
0.01350.00116371
1663
0.01430.00083923
2651
0.01400.00302430
316

Source: The authors.

One result that stands out in Table 4 is the extreme asymmetry of the numers of
low and high R&D intensity firms in the U.S. sample. Given that the threshold estimates
seek to approximately balance the weighted distance of the observations form thedelineating thresholds and the fact that the U.S. in Table 2 and in Appendix 2 shows acore set of firms with exceedingly high R&D intensities, i.e., high R&D/Asset ratios, it
should be expected that the high R&D intensities are associated with a relatively small
number of high R&D intensity firms as shown for the U.S.
We are also interested in comparisons of the threshold values for our threecountries, China, Japan, and the US. Based on the t statistics test results, we conclude tha
there exists minimal difference between the three countries’ thresholds. This finding isinteresting and intriguing. Our finding shows that the threshold for R&D intensity isapproximately 1.3%-1.5%. Whether this similar pattern is the optimal level across thewider set of economies and whether it can have an economic explanation is promising
gist for further study.
Given the determination of the respective thresholds for each country we analyze
the results for our full model applied to each of the three countries. We note that ourestimation model includes fixed effects for not only time, but also for the industry effects.
Our estimation equation includes industry dummies for six sectors: Drugs (including
biology and life science), Chemicals (Chemicals, Petroleum, Rubber & Plastic),Computers and Communications, Electrical (Industrial, Electric & Electronic Machinery),
Metals (Metals & Metal Products), and miscellaneous (Miscellaneous Manufacturing,
low-tech industry), and interact them with the knowledge ratios. BVD sectors in our
database determine the sectors.

41

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

6.2. Cross-country Comparisons
To determine whether our result is robust across countries, we apply the same nonlinear
threshold regression using Japanese and US data. For purposes of comparison, we limit
or period to the same 2013-2018 period for the regressions for Japan and the U.S. There
are 4081 observations in Japan and 3239 observations in the US. Since we estimate ourthreshold based on our data set, the threshold will differ in different groups. Hence, theR&D intensity threshold is 0.143 in Japan and 0.140 in the US, respectively. As explained
above, givent the singular robustness of a single threshold, the observations are distributeonly across high and ow intensity firms. The results are shown in the following Table 5.
Table 5: Cross-country Comparisons, 2013-2018
Nonlinear Model with Dependent Variable: log Tobin’s Q(with year and industry fixed effects)(1) China(2) Japan
R&D intensity > threshold, high R&D intensity firms
R&D/Assets
0.393***
0.158***(12.220)(7.751)Patents/R&D
0.226***-0.076***(10.431)(-5.826)
Publications/Patents 0.118***
0.090***(5.834)(5.997)
R&D intensity ≤ threshold, low R&D intensity firms
R&D/Assets
0.238***
0.065***(5.900)(3.951)Patents/R&D
0.253***-0.036***(6.681)(-2.621)
Publications/Patents 0.180***
0.025(4.301)(1.526)(3) US
0.137***(8.692)-0.042***(-3.517)0.086***(6.920)0.215***(5.332)0.031(1.139)-0.042(-1.473)

Note: Estimation method: nonlinear least squares and panel threshold regression. All equations
include a complete set of year dummies and six industry dummies.
Source: The authors.

Table 5 reports the semi-elasticity coefficients obtained from estimates of Eq. (3).
Column (1) is shows the estimates of our full model for China, columns (2) and (3)represents the full model estimation for Japan and the US. The Table shows that for the
R&D intensive firms, all of the estimates are statistically robust at the 1% level. For the
low-intensity R&D firms, whle each of the three estimated coefficients is significant at

42Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

the 1% level for Japan, the publication intensity coefficient is not significant, nor are thepatent and publication intensity estimates for the U.S. firm sample.
Since the model is non-linear, we cannot directly assess the impacts of theknowledge intensities on Tobin’s Q. Given the large difference in the mean and medianvalues for a number of the country variables shown for the full sample in Table 2, wecompute the median values for the high intensity and low intensity R&D firms; we use
these to compute the respective marginal products of the knowledge intensity measures.
These are reported in the bottom panel of Table 6.
Table 6: The Marginal Product of the Knowledge Stock Intensities
on Tobin’s Q using the median values of each of the variables
Median Values
China
R&D intensity > 0.0135, high R&D intensity firms
R&D/Assets
0.022
Patents/R&D
0.045
Publications/Patents
1.000
R&D intensity ≤ 0.0135, low
R&D intensity firms
R&D/Assets
0.029
Patents/R&D
0.006
Publications/Patents
1.000
Semi-elasticity
R&D intensity > 0.0135, high R&D intensity firms
𝜕𝜕 log 𝑄𝑄
0.263
𝜕𝜕 (𝑅𝑅&𝐷𝐷/𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)
𝜕𝜕 log 𝑄𝑄
0.100
𝜕𝜕 (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃/𝑅𝑅&𝐷𝐷)
𝜕𝜕 log 𝑄𝑄
0.897
𝜕𝜕 (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃/𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃)
R&D intensity ≤ 0.0135, low R&D intensity firms
𝜕𝜕 log 𝑄𝑄
1.469
𝜕𝜕 (𝑅𝑅&𝐷𝐷/𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)
𝜕𝜕 log 𝑄𝑄
1.170
𝜕𝜕 (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃/𝑅𝑅&𝐷𝐷)
𝜕𝜕 log 𝑄𝑄
0.950
𝜕𝜕 (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃/𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃)
Marginal ProductR&D intensity > 0.0135, high R&D intensity firms
𝜕𝜕𝜕𝜕
0.393
𝜕𝜕 (𝑅𝑅&𝐷𝐷/𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)
43

Japan

US

0.028
0.001
2.889

0.103
0.001
1.472

0.005
0.003
1.724

0.007
0.001
1.077

1.280

5.051

0.250

0.822

0.334

0.325

0.849

3.350

0.733

1.358

0.576

0.905

0.158

0.137

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

𝜕𝜕𝜕𝜕
0.226
𝜕𝜕 (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃/𝑅𝑅&𝐷𝐷)
𝜕𝜕𝜕𝜕
0.118
𝜕𝜕 (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃/𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃)
R&D intensity ≤ 0.0135, low R&D intensity firms
𝜕𝜕𝜕𝜕
0.238
𝜕𝜕 (𝑅𝑅&𝐷𝐷/𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)
𝜕𝜕𝜕𝜕
0.253
𝜕𝜕 (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃/𝑅𝑅&𝐷𝐷)
𝜕𝜕𝜕𝜕
0.180
𝜕𝜕 (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃/𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃)

0.076-0.042

0.090

0.086

0.065

0.215

0.036

0.031

0.025

-0.042

Source: The authors.

Starting with China, we compare the within country results for the high intensity
R&D firms and low-intensity R&D firms. We first note that given that Q = V/Assets, thecoefficient estimate for𝜕𝜕𝜕𝜕𝜕𝜕(𝑅𝑅&𝐷𝐷/𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)

shown in Table 5 can be interpreted as either themarginal product of Tobin’s Q with respect to R&D intensity, or the marginal product of
the firm’s total market value,V, with respect to R&D expenditure, ∂V/∂R&D. In eithercase for the high-intensity and low-intensity R&D firms, the estimates are 0.393 and0.238 respectively.
The analysis of this finding is not straightforward. That is, these estimates areconditional on the controls for patent intensity and publication intensity. As such, weinterpret estimates of the coefficients for𝜕𝜕𝜕𝜕𝜕𝜕(𝑅𝑅&𝐷𝐷/𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)

as estimates of the impact of R&D

intensity as the residual effect once we account for the impact of patent and publication
intensity. Hence, we analyze the marginal contributions of patent and publication
intensity and then return to interpret the impact of R&D intensity on Tobin’s Q.
Patent Intensity: As shown in Table 6, for the R&D intensive firms, Japan and the
U.S. report far lower levels of patent intensity than China. While these differences persist,
they are not as large for the low R&D intensity firms. These differences may result fromat least two different possibilities. First, it is possible that Japanese and U.S firms, in fact,
spend more on each patent, causing the returns to Japanese and U.S. patent intensity to
be closer to ne in the Scotchmer Diagram (Figure 1), that is, close to zero, than China.
Hence, the combination of high R&D intensity and low patent productivity.
An alternative possibility is that as compared with China, Japan and U.S. firms
patent a smaller portion of their patentable inventions, possibly because the patent
44

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

approval process is too time consuming and requires a description of the invention.
Seeking to achieve first-mover status by successfully staying one or more rungs ahead of
the competition, by intensifying their R&D effort and relying on secrecy methods andlegal protection, Japanese and U.S. firms are able to augment the probability of earlier
success, while securing secrecy, if over shorter durations. These two interpretations are
not mutually exclusive; in order to retain their technological advantage, Japanese and U.S.
firms may need to outspend their competition on frontier innovation. Moreover, once thetechnology of the first mover comes on line, the follower companies are likely to spend
less per innovation as they seek to imitate, improve, and adapt the emergent technologies.
China’s high Patent/R&D intensity may result from one or a combination ofseveral issues. One notable reason is that a portion of Chinese patent approvals are utility
and design patents of lower quality and shorter duration than the invention patents thatare closer to high international standards. Within our data set that share appears to besmall, while as much as two-thirds in some Chinese firms, the average is approximately
5%. As explained by Jiang and Jefferson (2021), other possibilities include the incentives
for large patent counts spurred by subsidies and rewards for patent production provided
by various levels of government. As a result of the heightened focus and return topatenting, firms sometimes convert single patents that might have condensed multiple
claims into a single patent into multiple patents, each with fewer claims.
Publication Intensity: This heterogeneity of patent quality could explain why
China has the highest return on publications per patent, which we use as a proxy for patentquality. Chinese firms with high publication to patent ratios also have relatively highproportions of invention patents. The importance of publication intensity is still greaterfor China’s low-intensity R&D firms for which the lower quality patents are likely to be
more prevalent.
For the high-intensity R&D firms, the returns to publications and publication
intensity are relatively evenly distributed across the three countries, with small declines
in their marginal returns from China to Japan to the U.S. Apart from China, the returns topublishing fall off substantially from high-intensity to low-intensity R&D firms. This is
likely due to the fact that the specialization and comparative advantage of the highintensity firms lies in the area of technology development for which the returns to highquality innovation and reputation are likely to be most pronounced.
R&D Intensity: As explained about, we interpret estimates of the returns to R&D
intensity as the residual return, once the contributions of patent and publication intensity

45

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

are accounted for. Our estimation results indicate that an increase in R&D intensity, i.e.,
the ratio of R&D to fixed tangible assets, may result in the following outcomes:
 Reduce patent/R&D intensity and, as suggested by Figure 1, increase the patentproductivity of R&D effort; Increase patenting and R&D spending in equal proportions thereby leaving themarginal product of patent intensity unchanged, with a likely increase in thespillover benefits of patenting, such as the reputation effect as well as a reductionin the marginal contribution of R&D intensity to Tobin’s Q.
 Increase the overall contribution of R&D through avenues other than those thataugment the steady-state stock of patents and publications, including conferences,
networking, purchases of recent explansions of R&D capabilities that have not yet
translated into higher patent output, and innovations for which patents are notapplied or approved.
 Enhance reputation effects without increasing patent or publication intensity.
Again, we see that the returns to China’s high-intensity R&D firms is more than 50%
higher than their low-intensity counterparts. Across the three countries, for both high andlow-intensity R&D firms, China’s marginal returns to R&D intensity outpaces those forJapan and the U.S., although for low-intensity firms, the marginal returns are comparable
for China and the U.S. These comparisons indicate that notwithstanding its surge in R&D
spending and intensity over the recent decades, the returns to R&D, more so for China’s
R&D intensive firms, remains robust. Moreover, for the high intensity R&D firms, thereturns to patent and publication intensity remain high. Whether constrains that the U.S.
and certain other countries have imposed on some of China’s most technologicallyadvanced firms has substantially diminished their returns to research and development
remains unknown. As the data accumulate for the current year and beyond, the impactsof these limits on the cross-border transfer and sale of technology will become moreevident.
In the last panel of Table 7 below, we show the simple marginal products for thefirm samples for each country with respect to the returns to the firm’s total market value,
V with respect to R&D expenditure, the patent count, and the publication count. While
the relative magnitues are similar to the relative values of the returns to knowledge
intensity, several of the signs and absolute values change significantly.

46

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Table 7: The Adjusted Marginal Product of Knowledge Stocks
on Market Value (the median firm)
China
Japan
Adjusted Marginal Product, year dummies deliminatedHigh R&D intensity firms
𝜕𝜕𝜕𝜕
0.393
0.158
𝜕𝜕(𝑅𝑅&𝐷𝐷)𝜕𝜕𝜕𝜕
16,2706,580
𝜕𝜕(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃)
𝜕𝜕𝜕𝜕
680
630
𝜕𝜕(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃)
Low R&D intensity firms
𝜕𝜕𝜕𝜕
0.238
0.065
𝜕𝜕(𝑅𝑅&𝐷𝐷)
𝜕𝜕𝜕𝜕
178
22
𝜕𝜕(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃)
𝜕𝜕𝜕𝜕
168
26
𝜕𝜕(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃)

US

0.137
2,060
420

0.215
41
37

Note: Unit: per dollar
Source: The authors.

We first note that given that Q = V/Assets, the coefficient estimate for𝜕𝜕𝜕𝜕𝜕𝜕(𝑅𝑅&𝐷𝐷/𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)

shown in Table 5 can be interpreted as either the marginal product ofTobin’s Q with respect to R&D intensity, or the marginal product of the firm’s total
market value with respect to R&D expenditure, ∂V/∂R&D. In either case for the high-intensity and low-intensity R&D firms, the estimates are 0.393 and 0.238 respectively.
We note several features of Table 7. The first is that while the total market value
returns continue to differ substantially as between patent intensity and simple patentcounts, Japan and the U.S. continue to earn positive, non-zero returns. Also while wecontinue to see higher returns to publications for China, the disparities as not s great asthey shown for publication intensity in Table 6 above. Overall, Table 7 confirms as shownin Table 6, China continues to have possibilities for augmenting its innovation intensity
and output while exhibiting returns that show well in relation to Japan and the U.S.

47

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

7. Conclusion
Combining the model of Hall et al. (2005) with an adapted measure for patent quality
with Hansen’s approach (1999) to separating the critical explanatory variable, R&D
intensity in our case, into low and high-intensity firms and using firm-level data tocompare China, Japan, and the U.S., we are able to compare the returns to Tobin’s Q fromthree measures of R&D intensity – R&D/assets, Patents/R&D, and Publications/Patents.
Among their findings, Hall, Jaffe, Trajtenberg (2005, p. 17) report:
We estimate Tobin's Q "hedonic" equations on three complementary aspects ofknowledge stocks: R&D "intensity" (the ratio of R&D stocks to the book value of
assets), the patent yield of R&D (i.e., the ratio of patent count stocks to R&D stocks),
and the average citations received by these patents (i.e., the ratio of citations topatent stocks). We find that each of these ratios has a statistically and economicallysignificant impact on Tobin's Q. This confirms that the market values R&D inputs,
values R&D output as measured by patents, and further values "high-quality" R&D
output as measured by citation intensity.
With the exception of substituting “average publications” for “average citations,” ourconclusion for China could mimic that of Hall et al. This conclusion is somewhat more
applicable to China’s high intensity R&D firms than its low intensity firms.
A certain takeaway is that firms representing the two sample of large firms in
China as compared with Japan and the U.S. manage their research programs strikingly
differently. The Chinese firms are clearly more highly rewarded than their Japanese andU.S. counterparts for high patent intensity. Japanese and U.S. firms appear to be utilizing
avenues other than patent counts to produce and secure their intellectual property. Thisfinding may well highlight a critical shortcoming of our study. That is, that the measureof publications per patent may not be an effective proxy for patent quality. Nonetheless,regardless of difficulties associated with cross country comparisons, our within-China
results yield a clear finding, which is that at the margin, the country’s high-intensity R&D
firms generate both higher returns to innovative activity than their counterpart lowininsity R&D firms.
This research invites further investigation into the sources of quality differences
between Chinese, Japan, and U.S. patents. Our proxy of publication intensity many notserves as a sufficiently robust measure of the value enhancing features of a given patent48

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

count. A second step concerns the need to more specifically identify certain of themeasures of intangible assets that extend beyond the included measures of patents andpublications. The fact that with a single exception, that of publication/patents for China’s
high R&D intensity firms, the returns to R&D intensity separate from patents andpublication exceeds patent and publication returns, in China, Japan, and the U.S. argues
for a more rigorous identification of the benefitting outputs of the R&D process.

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(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

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O’Mahony, eds., Productivity, Innovation, and Economic Performance, New York:
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Scotchmer, S. (2004) Innovation and Incentives, Cambridge: The MIT Press.

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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Appendix 1: Summary Table of Literature
Panel threshold regression model
Title / Author
Do Large Firms
Benefit More fromR&D Investment?

SampleNasdaq-listedcompanies forthe period
2002 to 2017

Variables
Thresholdvariable:
Firm size

Model
Panel thresholdregression

Dep var:Firm
performance

Oyakhilome
Ibhagui

Control var:
ROA, Tobin’s
Q, ROE, etc.

Panel SmoothTransition
Regression modeland an application
to investment undercredit constraints
Andres Gonzalez,
Timo Terasvirtabc,& Dick van Dijk
Threshold effects
of inflation on
growth in the
ASEAN-5
countries: A Panel
Smooth Transition
Regression
approach
Su Dinh Thanh
The Influence of
CSR on Firm
Value: Anapplication of panelsmooth transition
regression in
Taiwansame
economic
problem anddata set as
Hansen(1999a)Thresholdvariable:
Firm’s debt
level

ASEAN-5
countries(Indonesia,
Malaysia, thePhilippines,
Thailand, andVietnam) andthe period of
1980-2011

Thresholdvariable:
Inflation

Taiwan firms,
2010-2012

Threshold:corporate social
responsibility
(CSR)

PSTR

Dep var:Firm’sinvestment

Findings
R&D can haveeffects of varying
magnitudes on firm
performance,
depending on firm
size.
When R&D
weakens firm
performance, theadverse effects are
more pronounced
for small-sized
firms, but when theimpact of R&D ispositive, largesized firms tend toreap most of thebenefits.
Standard
asymptotic theory
can be used as thelikelihood functionin the PSTR model
is a continuousfunction of theparameters.

PSTR
Two regimes

Dep var:
GDP growth

Dep var:51

PSTR
One breakCross effectwith othercontrol variables

We concluded that
CSR investment
does not enhancecompany value
until it exceeds thevalue transition
threshold.

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Roger C. Y. Chen
& Chen-Hsun Lee
Foreign Direcet
Investment, Human
Capital andEconomic Growthin Africa: A Panel
ThresholdRegression
Approach

Sub-Sahara
Africa2002-2013

Firm
performance/
market value
Threshold:
FDI from China,
the US, EU
scored in years

PSTR, withextra nonlinear
component

Our findings revealthat Africa is shortof quality humancapital stock
required to absorb
advancedknowledge
embodied in FDI
from both its
traditional andemerging investors

Panel smooth
thresholdregression

Evidence of a Ushaped relationshipbetween electricity
consumption andtemperature

Dep var:
GDP per capita

Marvelous Ngundu

Electricity Demand
Elasticities andTemperature:
Evidence frompanel smoothtransition
regression with the
instrumental
variable approach

24 OECD
countries fromthe period
1978–2004

Chien-Chiang Lee& Yi-Bin Chiu

Thresholdvariable:laggedvariables,
including logtransformed per
capita real GDP
at t-1
Dep var:log-transformed
per capita
electricity
consumption

Evidence that inverted-U relationship or at least a decreasing margin benefit
between R&D intensity and firm performance/patent/survival
Inverted-U
Relationshipbetween R&D
Intensity andSurvival: Evidence
on scale andcomplementarity
effects in UK data

37,930 of
R&D-active
UK firms over
1998–2012

age, size,
productivity,
relative growth,
R&D intensity

Schumpeterianmodels ofcompetition,
innovation, andgrowth, Survival
model, Machinelearning model

Employment
rate,

productivity
growth=f(GER

Mehmet Ugur,
Eshref Trushinb, &Edna Solomon

What Is the
Optimal Rate of52

The relationshipbetween R&D
intensity and firm
survival follows aninverted-U patternthat reflects
diminishing scaleeffects.
R&D intensity andmarketconcentration arecomplements in that
R&D-active firms
have longer survival
time if they are in
more concentrated
industries.
The econometricanalysis shows that

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

R&D Investment
to Maximize
Productivity
Growth?

GDP per capita,
GREDD) is a concave
function
downwards.
Panel
regression, fixed
effectMario Coccia

2SLS

Evidence on the
Optimal Level of
Research &Development(R&D) Expenses
for KOSPI-listed
Firms in theDomestic CapitalMarketHanjin Kima
The Optimal Rateof R&D
Expenditurs in
GDP – Between
Theory andpractice
Steliana Sandu
Optimal Financing
for R&D-intensive
Firms

firms listed on
the KOSPI
stock market,
2010-2015

Indep var:
R&D, ROA, etc.
Dep var:Stock priceSome Eurocountries,
2007-2015

Case study,
Input-output
study

Initial R&D,
cash flow, risk
shield, etc.

Stochastic
financial model
BS formula

Richard T. Thakor& Andrew W. Lo

adverseselection andmoral hazard

Source: The authors.

53

risk-averse
functionpricing options

more than 65
percent of
productivity growthvariance is due to its
dependence on grossdomestic
expenditure on R&D
expressed as apercentage ofGDP(GERD).
The research showsthat the GERD range
between 2.3 percent
and 2.6 percent
maximizes the longrun impact on
productivity growth.
Three explanatory
variables, such as
R&D expenses ofthe prior fiscal year,
profitability, andTobin’s Q, showedstatisticallypronounced effects
to account for thelevel of R&D
spending.
The characteristics
of different countries
determine theoptimal rate of r&d.

A firm may use alimited amount ofdebt if it has
pledgeable assets inplace.
The analysis
highlights thepotential benet of anintermediationassisted coordinating
mechanism between
investors and RMS

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Appendix 2: Top 5 Firms in R&D Intensity
Top 5 R&D intensity firms
R&D intensity (R&D/Assets; 5-year average)
China
CANSINO BIOLOGICS INC.
SHANGHAI HENLIUS BIOTECH, INC.
VISIONOX TECHNOLOGY INC.
GETTOPACOUSTIC COMPANY LIMITED
VENUS MEDTECH (HANGZHOU) INC.
Japan
MEDRX CO LTDHEALIOS K.K
NANOCARRIER CO LTD
ONCOTHERAPY SCIENCE INC
3-D MATRIX LTDUSCATALYST BIOSCIENCES, INC.
CLOVIS ONCOLOGY, INC.
MANNKIND CORPORATIONPARATEK PHARMACEUTICALS, INC.
IMMUNE PHARMACEUTICALS INC

90.58569
49.23622
1.261864
1.007977.8533481
116.7699
38.11973
19.21855
13.87876
12.34683579
3219.603
2900.629
2877.931
2820

Note: Top 50 are all US firms.
Source: The authors.

Other notable firms
APPLE INC.
ALPHABET INC.
TESLA, INC.
SONY CORPORATIONTHK CO LTD
HONDA MOTOR CO LTDPINTEREST, INC.
ZOOM CORPORATION
Source: The authors.

0.0505204
0.163784
0.6629741
0.0577596
0.0257945
0.0567193
0.3329162
0.1172597

54

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Appendix 3: Additional Regression Results (Showing results forindividual industries)
Full Regression Result, Adding Industry Effects, 2013-2018
Nonlinear Model with Dependent Variable: log Tobin’s Q(1) China(2) Japan

R&D intensity > threshold, high R&D intensity firms
R&D/Assets
0.329***
0.307***(20.599)(19.384)
Patents/R&D
0.125***
0.060***(13.511)(5.262)Publications/Patents
0.112***
0.080***
0.329***
0.307***
Business
0.056-0.009(0.961)(-0.086)
Chemicals
0.341***
0.161***(11.781)(2.956)
Computers
0.255***-0.110*(6.250)(-1.771)
Electrical
0.134***
0.225***(5.507)(4.818)Drugs
0.346***-0.185(3.847)(-0.800)
Miscellaneous
0.266-0.004(1.213)(-0.059)
R&D intensity ≤ threshold, low R&D intensity firms
R&D/Assets
0.258***
0.168***(11.256)(3.632)
Patents/R&D
0.207***
0.145***(9.929)(6.058)Publications/Patents
0.168***
0.114***(7.623)(4.123)Business
0.062-0.258(0.493)(-1.027)
Chemicals
0.263***-0.061(4.153)(-0.472)
Computers
0.756***
0.067(4.624)(0.276)
Electrical
0.092-0.272**(1.243)(-2.351)
Drugs
0.868**-0.881***(2.567)(-3.432)
Miscellaneous
0.000
0.000
(.)
(.)

(3) US(4) ThreeCountries

0.295***(11.632)
0.048***(3.115)0.019
0.295***
0.957***(7.707)0.133***(3.029)0.679***(8.302)0.229***(5.762)0.386***(2.869)0.885***(5.686)

0.349***(39.002)
0.053***(7.953)-0.006
0.349***
0.212***(4.140)0.218***(9.205)0.259***(7.830)0.220***(10.838)
0.292***(3.765)0.226***(3.816)

0.037**(2.241)0.015(1.180)0.010(0.656)0.684***(9.331)0.108(1.468)1.095***(14.627)0.140***(2.842)
1.124***(5.636)0.132(0.335)0.116***(8.140)0.080***(6.862)0.026**(2.024)0.354***(4.848)0.341***(7.220)0.843***(10.371)0.101**(2.191)0.627***(3.626)-0.040(-0.075)

Note: Estimation method: nonlinear least squares and panel threshold regression. All equations
include a complete set of year dummies.
Source: The authors.

55