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Does Enforcement of Intellectual Property Rights Matter? Evidence
Transcript of Does Enforcement of Intellectual Property Rights Matter? Evidence
Does Enforcement of Intellectual Property Rights Matter?
Evidence from Financing and Investment Choices in the High Tech Industry
James Ang, Yingmei Cheng, and Chaopeng Wu
Florida State University
First draft: October 28, 2008
Revised: November 15, 2008
Does Enforcement of Intellectual Property Rights Matter?
Evidence from Financing and Investment Choices in the High Tech Industry
Abstract
Financing of and investing in R&D are prone to risks of appropriation by competitors, information
asymmetry, and agency problems. Although legal protection of intellectual property (IP) rights at the
national level is necessary to encourage investing in R&D, we show that the effective enforcement at
the local level is also critical. We concentrate on the impact of IP rights enforcement at the provincial
level on the financing of and investing in R&D, using a unique and rich sample of high technology
firms. These firms are located in twenty-eight provinces/districts throughout China. The enforcement
of IP rights differs at the provincial level, although the firms are under the same set of national and
international laws. Controlling for provincial institutional factors such as economic development,
banking system development, legal system performance, and local government corruption, we find
that the enforcement of IP rights positively affects firms’ ability to acquire new external debt
(including formal and informal financing) and external equity. The firms in provinces with better
enforcement of IP rights invest more in R&D, generate more patents, and produce more sales from
new products. We also find better enforcement of IP rights helps mitigate the problem of appropriation
by local partners in foreign and ethnic joint ventures. Our evidence confirms that enforcement of IP
rights matters even in China. Furthermore, our results support that the enforcement of IP rights affects
the growth in the economy via the channels of financing of and investing in R&D.
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Does Enforcement of Intellectual Property Rights Matter?
Evidence from Financing and Investment Choices in the High Tech Industry
1. Introduction
Allen, Qian and Qian (2005) have raised the puzzle that “China’s legal and financial systems as well
as institutions are all underdeveloped, but its economy has been growing at a very fast rate.” More
specifically, it is a challenge to explain the high growth in R&D in China in spite of its generally
perceived low protection of intellectual property (IP) rights. The growth of China’s R&D expenditure
ranks first among 40 OECD countries and selected non-member economies from 2002 to 2006 (OECD,
2006), while its intellectual property protection is still considered very weak in comparison to other
countries (Israel, 2006; Stratford, 2006; International Intellectual Property Association, 2007). The China
phenomenon has become such an anomaly, as some cross-country studies are unable to obtain the
expected positive relation between intellectual property protection and economic growth unless China is
excluded from the sample (Gould and Gruben, 1996). Does this lead to the inference that China is
somehow different and intellectual property protection does not matter there? This is the question we
address in this paper.
We investigate the impact of local level enforcement of IP rights on the financing of and investing in
R&D in China. Effective protection of intellectual property rights depends both on the existence of
intellectual property protection laws and the enforcement of the laws.1 Although much has been written
about the IP rules and laws (e.g., Gould and Gruben, 1996; Moser, 2005), there is little empirical evidence
of the importance of enforcement. One reason is that studies of intellectual property protection are
generally performed at country level.2 Country level analysis does not allow researchers to separate the
1La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998) state that “a strong system of legal enforcement could substitute for weak rules” (p.1140).The cross-country studies of La Porta et al. (2006) and Jackson and Roe (2008) have documented the role of private and public enforcement of securities laws. 2Comparisons are made on national intellectual property laws, but not the quality of their enforcement. Existing empirical evidence suggests a positive effect of the extent of IP laws on GDP growth (Gould and Gruben, 1996), direction of technical change (Moser, 2005), and foreign direct investment (Javorcik, 2004; Du et al., 2008). The
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confounding effects of the existence of the IP laws and the effectiveness of the enforcement. We deal with
cross country variations in IP laws by focusing on a single country, China.
To our knowledge, our paper is the first to investigate the relationship between provincial-level
enforcement of IP rights and firm-level financing of and investing in R&D. We do not treat China as a
single homogeneous entity. Rather we recognize that even though the applicable intellectual property laws
and international treaties are the same within China, there exist significant differences in the local
enforcement of the IPP laws. Our approach of studying provincial variations is similar to that of Guiso,
Sapienza and Zingales (2004) who study the difference in regional social capital in Italy, and Benfratello,
Schiantarelli, and Sembenelli (2006) who investigate the effect of local banking development on firms’
innovative activities in Italy. We analyze the impact of the local enforcement of IPP laws on financing of
and investing in R&D by firms in various provinces throughout China.3
We propose that better enforcement of IP rights mitigates the problems associated with R&D: risks
of appropriation by competitors, information asymmetry, and agency problems. This leads to the
empirically testable propositions that better enforcement of IP rights would lead to more funds available
to finance R&D, more investment in R&D, and more productive output from R&D. For the firms that are
joint ventures between local and foreign partners, there is the agency problem of appropriation by one of
the partners within the joint ventures. When the enforcement of IP rights is poor, the risks of appropriation
by one of the business partners increase in these joint ventures. Given that the local firms are generally
inferior in technology, foreign firms would hesitate to transfer or invest in technology in joint ventures if
local enforcement of IP rights is poor. We hypothesize that better enforcement of IP rights enables greater
technology transfer and development by restraining the local partners from appropriating the technology.
To test our hypotheses, we utilize several unique data sets that have not been examined in previous
empirical studies. The database compiled by the Ministry of Science and Technology of China (MOST)
impact of intellectual property protection on the number of innovations and R&D investment are also widely studied (e.g., Nordhaus, 1969; Sakakibara and Branstetter, 2001). However, most of the studies are at country level. 3 The Office of the US Trade Representatives in its June 2006 review of intellectual property rights protection in China switches its emphasis from country based assessment of China to developments at the provincial level in China (Federal Register 43,969, June 16 2006; also see Yu, 2007).
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provides firm-level financial information, ownership type, and R&D related information on a large
number of Chinese high tech companies. These firms are also unlisted companies. We choose to focus our
study on these unlisted high tech firms, because R&D is critical in the success of their operation. We also
obtain, from a number of sources, various measures of IP rights enforcement in each province or special
district in China. Among them are the number of intellectual property law offices, size of technology
transfer market, the number of patent infringement cases, and the number of closed cases. In addition, we
collect and use provincial level information to capture the local economic development, banking
development, legal system performance, and the extent of government corruption.
We show that the high tech firms in provinces with better enforcement of IP rights enjoy greater
access to external financing, invest more in R&D, and generate more patents and more new product sales.
Establishing that IP rights enforcement has a positive impact on R&D, our results demonstrate that
protection of intellectual property rights matters even in China. Better enforcement helps to facilitate
financing of and investing in R&D, and therefore stimulate economic growth.
Our evidence points out that some but not all provincial authorities may know that the IP rights
enforcement matters. Provinces in China having better enforcement of IP rights at the beginning of our
sample period (thus realizing the economic benefits of greater investments in R&D by high tech firms)
keep improving the quality of the enforcement. On the other hand, we do not observe improvement in the
enforcement over time for the provinces ranked in the lower half in terms of IP rights enforcement at the
beginning of the sample period.
We also find that poor IP rights enforcement exacerbates the agency problem of appropriation by
local partners, and better enforcement of IP rights mitigates this type of agency problem. Technology rich
foreign companies in joint ventures with local Chinese firms in poor enforcement provinces are more
reluctant to finance and invest in R&D. However, foreign partners of joint venture firms in provinces of
good IP rights enforcement are more willing and able to obtain external financing. They also invest more
in R&D and are more productive in introducing new products.
In recent years, there have been numerous studies on the role of financing in economic growth (King
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and Levine, 1993; Rajan and Zingales, 1998; Beck et al., 2000). These studies either compare financial
development and economic growth across countries, or examine changes in economic growth from before
to after financial market liberalization (Bekaert, Harvey, and Lundblad, 2005). Additional empirical
analysis at the firm level is needed to provide insights into how financing leads to economic growth. We
concentrate on financing of and investing in R&D, as technological innovation is the most significant
component of real economic growth. One broad objective of our paper is to provide firm level evidence
that financing of and investing in R&D are the channels that link enforcement of IP rights and economic
growth.
The rest of our paper proceeds as follows. Section 2 presents the testable empirical hypotheses.
Section 3 describes the database of high tech companies, the measures of the provincial IP rights
enforcement, and variables of provincial institutional environment. Section 4 reports our empirical results
on the effect of IP rights enforcement on external financing, R&D input, and R&D output. Section 5
provides further analysis of the impact of IP rights enforcement on firms with different level of
intellectual property intensity, and with various ownership types, in particular, the joint ventures. Section
6 concludes.
2. Intellectual Property Rights Enforcement and Finance
In this section, we first describe IP rights enforcement in China, and then we discuss problems of
information asymmetry, risks of appropriation by competitors, and the agency conflict of appropriation by
local partners that are inherent in R&D. These issues could lead to under provision of funding and
underinvestment in R&D. Finally, we develop hypotheses of how better IP rights enforcement may
mitigate these problems.
2.1 IP rights enforcement in China
Although China is often criticized as having a poor record in the protection of intellectual property
rights (see Wang, 2004; Maskus, Dougherty and Mertha, 2005), an examination of China’s intellectual
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property laws (Appendix 1) and the international treaties it has acceded to (Appendix 2) would put China
at par with the more developed economies.4 Two things may explain the discrepancy between the written
laws versus the common perception. The first is that most of the intellectual property laws in China are
relatively recent. Almost all of the items listed in Appendix 2 were either enacted or amended after 2001,
which might be attributed to China’s membership in WTO that began in 2001. It may take time for the
laws to work and for the perception to adjust.
Figure 1 shows the amount of licensing fees in U.S. dollars paid by Chinese enterprises to foreign
countries from 2004 to 2007 (the data is obtained from the Ministry of Commerce of China). The paid
licensing fees grew substantially, increasing from $13.86 billion in 2004 to $25.42 billion in 2007. The
large numbers in Figure 1 suggests that there exists significant IP rights protection in China at least in the
recent period, contradicting the common perception.
Insert Figure 1
The second is that effective protection of intellectual property depends on the existence of IPP laws
and the enforcement of the laws. Although the laws and treaties are national, the enforcement is local.
One has to understand local differences in the enforcement. Even though the applicable intellectual
property laws and international treaties are the same within China, there exist significant differences in
the local enforcement of the IPP laws, as we find out in our study.
These two considerations suggest that a research would have more value by concentrating on local
differences in the enforcement and on more recent experience (2001 and later). We examine the IP rights
enforcement at the provincial level in China. Our focus is to study how provincial-level enforcement of IP
rights helps to resolve the problems associated with R&D.
2.2 Information asymmetry, risks, and agency problems
The competitive capabilities of high tech enterprises are largely determined by their intellectual
4Of particular importance is the membership in the three major agreements as identified by Park and Ginarte (1997): 1) the Paris Convention, 2) The Patent Cooperation Treaty (PCT), and 3) International Convention for the Protection of New Varieties of Plants (UPOV).
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property, such as patents, trade secrets, specialized manufacturing technologies and processes. However,
as suggested by Nelson (1959) and Arrow (1962), it is difficult for companies to internalize all positive
externalities and prevent free riding on their intellectual property. Innovative ideas of how to make new
goods and deliver new services could often be imitated and appropriated by their competitors. The risks
of appropriation by competitors have led to the result that private returns to R&D investments are lower
than their social returns, as documented by Griliches (1992) and Hall (1996). For these reasons, firms
tend to be reluctant to finance and invest in R&D, which in turn reduce a country’s economic growth.
Asymmetric information could also adversely reduce the amount of available financing and
investment in R&D. Inventors have better information about the likelihood of success and the nature of
their innovations than outside investors (Hall, 2002). In the context of our analysis, the problem of
information asymmetry refers to the fact that the firms are unwilling to disclose confidential information
on current and future plans to potential lenders/investors. Companies are reluctant to reveal their
innovative ideas or the stage of their development to external fund providers, because these fund
providers could steal the knowledge (Anton and Yao, 1998). Ueda (2004) analyzes a situation in which a
venture capitalist could pose a threat by stealing ideas and projects from the entrepreneur, and suggests
that stronger protection of intellectual property rights could mitigate the problem and encourage
entrepreneurs to seek financing from venture capitalists.
There is also the agency conflict of appropriation of the technology of one partner by another in joint
ventures. The benefits of a joint venture are in combining the complementary strengths of each party. For
instance, local partners may have established network or relationships to deal with government
bureaucracy.5 Local partners are also more familiar with the domestic market than foreign partners.
Foreign investors could provide advanced manufacturing technology, managerial skills, and assistance to
establish R&D facilities. However, Desai, Foley and Hines (2003) argue that foreign multinational firms’s 5Franko (1989), Gomes-Casseres (1990), and Contractor (1990) argue that sole ownership is generally preferred by multinational parents but occasionally they have to concede in the bargaining with the host governments to form joint ventures. Henisz (2000) and Gatignon and Anderson (1988) present evidence that multinational parents entering countries with higher political risk are more likely to use joint ownership since local firms are well positioned to interact with local government.
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willingness to share intellectual properties is limited by the fear of appropriation by their local partners. In
a study of technology transfers to Indian firms, Ramachandran (1993) finds that subsidiaries that are 100
percent owned by foreign multinationals receive greater technology transfers than subsidiaries that are
only partially owned by foreign multinationals.
2.3 The effect of IP rights enforcement on external financing
For those who infringe on others’ intellectual property, they have a higher probability of facing legal
consequences in provinces with better enforcement of IP rights. More effective enforcement raises the
costs of imitation and infringement, and also helps to generate a larger market for legal transfer of
property rights such as licensing fees, etc. Consistent with the model in Ueda (2004), better protection of
intellectual property rights would punish lenders/investors that steal information of innovations from
firms, and thus give the firms more confidence in disclosing confidential information to potential external
fund providers.
If better IP rights enforcement could help high tech firms to reduce risks of appropriation by
competitors and resolve information asymmetry, the high tech firms should be able to receive more
outside financing (Hypothesis 1).
2.4 IP rights enforcement and informal financing
China’s formal financial system is large but still underdeveloped; it is mainly controlled by the four
largest state-owned banks. In such a system, most of the bank credits are issued to companies in the state-
owned sectors. Although firms in the private sectors have played a critical role in China’s economic
growth, they face substantial barriers to obtain bank credit. As shown in Allen, Qian and Qian (2005),
Chinese firms in private sectors rely on bank loans to raise only about 10% of total financing, while state-
owned sectors depend more on banks for financing (more than 25% of total financing). These numbers
show that even in the state-owned sectors, bank loan is still not the main source of financing. Thus, we
have a financing aspect of the China puzzle: how could firms finance growth when the roles of formal
financing channels are relatively small and narrow (very few large banks) or virtually non existent (in
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corporate bond market)?
Filling the financing gap are various forms of informal sources of financing. Similar to the definition
in Ayyagari, Demirguc-Kunt, and Maksimovic (2008), informal financing is defined as “the entire gamut
of non-market institutions such as credit cooperatives, trade credit, underground informal money lenders,
family, friend, etc., that do not rely on formal contractual obligations enforced through a codified legal
system.” Some examples of informal financing include lending from friends, family, and community,
rotating savings groups, underground financial institutions, and inter-corporate lending (Farrell et al.,
2006; Ayyagari, Demirguc-Kunt and Maksimovic, 2008). While informal financing exists in most
countries with under-developed financial markets and institutions, what differentiates China from the rest
is the large size of the informal financing, in relative and absolute terms. For instance, it has been
estimated that lending from friends and family is as large as 800 billion dollars in 2004, which is 25% of
the total bank deposits.6 As surveyed by World Bank (Ayyagari, Demirguc-Kunt, and Maksimovic, 2008),
the informal sources account for 71% of debt financing for the Chinese companies. Similarly, in this
study, we find that the informal financing counts for more than 71% of debt financing by high tech firms
(Table 1).
On one hand, when these informal sources lend to high tech firms, they prefer regions with better IP
rights enforcement. Their return depends on the borrowers’ ability to generate cash flows. Poor
enforcement of IP rights could increase the probability and magnitude of appropriation of intellectual
properties by competitors, resulting in reduced expected cash flows. On the other hand, when the
borrowers disclose confidential information to the informal lenders, these lenders may divulge the
information to a third party such as other competitors they have financed (similar to the venture capital
situation modeled in Ueda, 2004). Firms in regions with poor IP rights enforcement are therefore
discouraged to seek financing from informal lenders. These two considerations, taken together, would
suggest that there is a positive relationship between the IP rights enforcement and the extent high tech
6For comparison, underground lending in China represents about $100 billion according to a recent McKinsey report (Farrell et al., 2006) and as suggested in Allen et al. (2005) and Ayyagari et al. (2008).
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firms seek financing from informal sources (Hypothesis 2).
2.5 IP rights enforcement, R&D input, and R&D output
Better IP rights enforcement raises the expected payoff from investing in R&D. Thus, we expect
better IP rights enforcement results in more investment in R&D by high tech firms (Hypothesis 3).
Firms in regions with better IP rights enforcement receive greater protection from patent
infringement, and therefore they are more likely to seek patent generation, registration, and application.
Therefore we expect better IP rights enforcement increases the number of patents generated by firms
(Hypothesis 4A). Poor IP rights enforcement makes stealing of intellectual property possible through
patent infringement, imitation, etc., and thus reduces new product sales. Therefore, we expect better IP
rights enforcement increases firms’ sales from new products (Hypothesis 4B).
2.6 IP rights enforcement and joint ventures
Another role of IP rights enforcement in stimulating external financing and R&D investment is that it
would help mitigate the agency problem of appropriation of the technology of one partner by another in
the joint ventures. We conjecture that when the IP rights enforcement is weak, in anticipation of agency
costs from the risk of appropriation by the local partners, technology transfers by foreign firms will be
withheld. We hypothesize that better IP rights enforcement could mitigate this agency problem. Joint
venture firms in regions with better IP rights enforcement are predicted to obtain more financing and
invest more in R&D, generate more patents, and produce more sales from new products (Hypothesis 5).
3. Measures of IP Rights Enforcement, Provincial Institutional Environment, and Database
Description
In this section, we describe several measures of provincial level IP rights enforcement in China and
indices of provincial institutional and economic environment. In addition, we specify the data sources
and present a preliminary description of the high tech firms in our sample.
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3.1 Measures of provincial level IP rights enforcement
We consider several ways of measuring enforcement of intellectual property rights. In the following,
we first present two measures that we regard as the best in capturing the provincial enforcement of IP
rights.7 Then we introduce and discuss other alternative measures we have also considered and analyzed.
Our first measure of intellectual property protection enforcement (IPP1) is the density of intellectual
property law firms at the provincial level, measured as the number of intellectual property law firms
divided by the size of the population in ten thousands.8 Provincial data on the number of intellectual
property law firms is obtained from State Intellectual Property Office of China (SIPO), and the
information of provincial population comes from National Bureau of Statistics of China (NBS). SIPO
makes an annual inspection of all intellectual property law firms and publicly announces the list of
qualified law firms every year9.
The intellectual property law firms are authorized by the Chinese government to represent clients in
handling all affairs concerning patent, trademark, copyright, integrated circuit layout design, software,
domain name, customs record of intellectual property rights, trade secret, cases involving unfair
competition as well as related litigation, technology transfer and licensing matters.10 IPP1 is similar to the
approach adopted by Benfratello, Schiantarelli, and Sembenelli (2006). They measure the banking
7Measures of IPP enforcement at country level are of two types: perception of IPP enforcement from a survey (see the studies cited in Lanjouw and Lerner, 1997), or existence of mechanism for enforcement. The latter is exemplified by the index constructed by Park and Ginarte (1997) where a country’s enforcement score is the sum of the availability of (1) preliminary injunction, (2) contributory infringement pleadings, and (3) burden of proof reversals. 8One might suggest that the ratio of the number of intellectual property law firms divided by the number of technical personnel in that province could be an alternate measure of enforcement of demand driven IP protection. We have substituted this measure in our estimations and find similar results. 9However, the data on the number of intellectual property law firms is only available from 2002, so we assume the numbers in 2001 are the same as in 2002. This assumption does not change our results, because the cross-province variation is more important in our study than the time-series variation for each province. 10The basic requirements to qualify as a patent lawyer in China are comparable to that in U.S and other developed countries, as they are probably modeled after them. According to State Intellectual Property Office of China (SIPO), these requirements are: 1) An intellectual property law firm must have at least three patent attorneys. 2) In order to be registered as a patent attorney, a lawyer must, in addition to passing the regular bar, pass the “registration examination” held by State Intellectual Property Office of China (SIPO), which is similar to “USPTO registration examination” in the U.S. This examination is commonly referred to as the “patent bar”, which tests a candidate’s knowledge of patent law and SIPO policies as well as patent examination procedures. 3) A candidate must also have an adequate scientific and technical background or education to understand a client's invention. The educational requirement can be met by a college degree in natural sciences or in engineering.
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development as branch density, calculated by the number of bank branches in a province divided by its
population.
The functions of intellectual property law firms and attorneys are as follows. First, they may act on
behalf of a company in applying for patents, trademark registration, and computer software copyright
registration, etc. Second, they may serve as the conduits to facilitate companies to collect royalties and
licensing fees and thus, reduce economic loss due to infringement and imitation. Third, when companies’
intellectual properties are infringed, they may act on behalf of the clients to take a full range of actions,
including conducting investigation of the violators, collecting relevant evidences, sending warning letters,
and negotiating for mediation. If all these measures fail, they may request administrative authorities to
investigate and deal with the infringement activities, and finally, file lawsuits in the courts. In practice, as
in elsewhere, most of the infringements cases are mediated by the intellectual property law firms before
taken to the courts; according to the data compiled by State Intellectual Property Office of China (SIPO),
87% of the infringement cases are resolved without a court order in 2006.
The density of the intellectual property law firms reflects the demand for IP rights enforcement. The
growth in the number of the law firms is in response to the decision of the “injured” firms to seek legal
redress and other formal remedies. Actual legal costs depend on the expected benefits of legal recourse;
when they are perceived to be low (high), infringement may (may not) be tolerated (Langouw and Lerner,
1997). The demand is a function of the perceived ability and determination of the provincial authorities
and courts to enforce IP rights in the region.11
Insert Table 1
Table 1 (Panel A) shows that the mean density of intellectual property agents is 0.007. The mean
density increases from 0.0064 in 2002 to 0.0073 in 2005 (not tabulated). What is most relevant for this
11The increase in the number of the law firms in a region could intensify competition for the service, resulting in lower price and improved service. Many of the law firms may make alliances to establish a network of monitors, through branch offices and affiliate relationships, against patent infringements and other violations. Finally, competitive pressures brought by the new entries will cause law firms in the regions to expand their markets by increasing the awareness of business firms to IP rights via dissemination of information, and to call attention to their services.
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study is the large cross-sectional variation across provinces, ranging from a low of 0.001 to a high of
0.089, with a variance of 0.015. The map in Figure 2 shows how this measure of IP rights enforcement
varies greatly within China. The enforcement is best in the coastal region of China, weaker in the
Northern China, and weakest in the Southwest and Central regions. However, even within these regions
there are substantial variations across provinces, which are reflected in our later empirical results.
Insert Figure 2
The second measure of IP rights enforcement (IPP2) is the size of the market for transactions in the
technological intellectual property, measured as the transaction volume of technology transfer in a
province divided by the provincial GDP. These transactions include royalties, licensing fees for patents,
and use of other intellectual properties. Annual data of the transaction volume and GDP in each province
is available from the Ministry of Science and Technology of China (MOST) and the National Bureau of
Statistics (NBS).
As Table 1 shows, the average size of the technology transaction market is 0.8% of provincial GDP.
There exists a large cross-sectional variation in the size of the technology transaction market. Some
provinces do not report any payment for technological transfers, while the transaction volumes of those
that do are as high as 7.1% of their GDP. Legal incomes to holders of intellectual property in China could
in fact be quite large, contradicting the common perception of little or no payment by Chinese firms to
use other firms’ intellectual property.
We use IPP2 to capture the fact that in regions with better IP rights enforcement, given common national
laws, regulations, and treaties, there will be fewer cases of illegal usurpers of intellectual property as they may
face greater penalty. Those needing certain technologies owned by others would have to negotiate and pay the
users’ fees, increasing the size of the market for technological transfer and use. Therefore, the size of the
technology transaction market reflects the effectiveness of the province’s IP rights enforcement.
While IPP1 captures the demand for services to protect intellectual property, IPP2 is an outcome-
based measure of intellectual property protection. Panel B of Table 1 reports the cross-correlation between
the two measures of IP rights enforcement. Despite the difference in the meanings and origins of these
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two variables, their correlation is high (the correlation coefficient is 0.896). However, they are still not
perfectly correlated and we may gain new insights by using both in the empirical analysis.
We also collect data to construct two alternative measures of IP rights enforcement: the number of
court cases concerning intellectual properties (on patents and copyright infringements) in a province, and
the percentage of cases closed. These measures, at first glance, appear to be directly linked with IP rights
enforcement. Since 1990s, there has been an increase in the number of judges who possess intellectual
property expertise in specialized courts in major cities; one ensuing result is that foreign rights holders
increasingly use the courts instead of administrative enforcement. However, a careful examination of the
data reveals some problems. For example, provinces with few or no high tech firms may have few
intellectual property disputes, and therefore a high rate of resolution. Because of these data concerns, we
hesitate to use them as our principal measures of IP rights enforcement. Nevertheless, they are positively
correlated with IPP1 and IPP2, and robustness tests with these two alternative measures in the regressions
produce qualitatively similar results.
3.2 Database of high tech firms
Ministry of Science and Technology of China (MOST) conducted an annual survey of high tech
companies from 2001 to 2005. The surveyed companies are approved as high tech enterprises by the local
Science and Technology Bureau, who are permitted to enter the National Innovative and High Technology
Industrial Development zones in selected cities of the provinces throughout China.12 The numbers of
companies in the surveys by year are: 8,298 (for 2001), 9,743 (2002), 11,470 (2003), 13,261 (2004), and
15,459 (2005). The surveyed samples are expected to cover all of China’s high tech companies in these
designated zones because the annual survey is compulsory for all the qualified companies.
The survey questionnaires collect information on balance sheet, income statement, ownership
structure, details of research and development expenditures, funding sources of R&D expenditures, R&D
personnel composition, and R&D output such as new product sales. These data, which are collected by
12In China, as in many countries, high tech firms in the special zones enjoy tax preferences and other policy support.
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MOST to monitor the operation and development of high tech companies, are potential inputs to future
policy making. This database has not been examined in academic studies.
We select our sample from the companies surveyed by MOST under these criteria: (1) the total asset
of the company is more than 10 million RMB (Chinese Yuan); and (2) for the purpose of constructing the
external financing variables, the companies have consecutive and complete records during the sample
period.13 We classify the sample firms into six types according to their dominant ownership types. These
are: state-owned enterprises (SOE), privately owned enterprises (POE), foreign-owned enterprises
(Foreign), ethnic Chinese owned enterprises (Ethnic), collectives-owned enterprises (COE) and others.14
Furthermore, foreign-owned enterprises could also be divided into foreign-Chinese joint ventures
(Foreign Joint Venture) and foreign solely owned enterprises (Foreign Solely Owned). Ethnic-Chinese
owned enterprises include ethnic Chinese and local Chinese Joint Ventures (Ethnic Joint Venture) and
ethnic Chinese solely owned enterprises (Ethnic Solely Owned). The companies in the financial industry
are excluded. Our final sample comprises of 16,225 (3,245×5) firm-year observations from 2001 to 2005.
In addition, we hand-collect the patent data by firm from the SIPO patent website. The patent data
includes the numbers of invention patents, utility model patents, and design patents created by a firm in a
given year.
In comparison to the databases used in previous research involving China, our database has at least
two notable features. First, the high tech companies in our sample are unlisted firms, and our database
provides detailed information about each firm’s R&D expenditure, R&D personnel, and R&D output.
This type of information is not normally required to be disclosed in the annual reports of listed companies,
and generally could not be found for unlisted companies. Second, compared to the database from the
National Bureau of Statistics (NBS), which only track manufacturing firms, our database covers
13We verify that there are no significant differences between the firms with complete records and those with missing records. In terms of financial health, in particular, there are no significant statistical differences in profitability (ROA) and in the percentage of observations with negative equity. We are confident that missing years for these firms is due to data reporting omission, and not survival bias. 14Others include the companies whose dominant ownership type cannot be determined based on the information in the survey data by MOST.
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exclusively high tech companies in various industries. The top three industries in our sample are
telecommunications (30%), equipments and instruments (30%), and biotechnology (9%).
3.3 External financing of the sample firms
To analyze the sources of external financing of high tech companies, we construct three variables.
First, firm i is coded as having raised new debt in year t if the net increase of debt for firm i in year t
exceeds 5% of its total assets at the end of year t. This 5% cut-off point is consistent with previous studies,
which is used to guarantee that the analysis focuses on relatively substantial financing events
(Hovakimian, Opler and Titman, 2001; De Haan and Hinloopen, 2003). More than 40% of firm-year
observations have raised new debt.
Second, we divide new debt into bank loan and informal financing. Given the limited information on
detailed debt structure, we compute informal financing ratio as the net increase of all debt in a given year
minus the net change in bank loan, and then scaled by the net increase of all debt. If net new debt is less
than new bank loan in a year, informal financing is negative, i.e., the firm repays previous borrowing
from informal sources with new bank loans. On the other hand, if new bank loan is negative and net new
debt is positive, informal financing is the source of funds to pay back previous bank loan. Because the
information on bank loan is available only in 2004 and 2005, this portion of the analysis is limited to
these two years. It turns out that we have 2,633 firm-year observations with the informal financing ratios.
As shown in Table 1, about 71% of new debt comes from informal financing for the 2,633
observations. By comparison, 25.9% of the total capital raised, including new debt, new equity and
retained earnings, comes from bank loans. This number (25.9%) is higher than the average percentage of
bank loans (20.36%) for the Chinese sample in Ayyagari, Demirguc-Kunt, and Maksimovic (2008), as
theirs includes mostly non high tech companies.15 The Chinese government has a policy to encourage
banks to provide special loans and working capital credit for science and technology projects of qualified
15When compared to the average percentages of bank loan for developing countries in South Asia (23%), Africa (19%), Latin America and Caribbean (21%), East Asia and the Pacific (32%, excluding China), East Europe and Central Asia (32%), our number is still higher than most of the countries and regions.
16
high tech companies.16 Despite of the policy support, about two third of our samples, which raised new
debt in 2004 and 2005, do not report to have received any bank loan and could only raise funds from
informal financing sources.
Third, firm i in year t is coded as having raised new external equity if the net increase of external
equity for firm i in year t exceeds 5% of its total assets at the end of year t. Consistent with the definition
of Baker, Stein and Wurgler (2003), new external equity is equal to the net increase in book equity minus
the net increase in retained earnings. Only 15.5% of firm-year observations have recorded new external
equity. Since our sample only includes unlisted companies, most of companies actually raise new equities
from parent companies or existing shareholders.
Table 1 (Panel A) reports the summary statistics for measures of external financing, R&D input, and
R&D output. We measure R&D input as R&D intensity, which is defined as research and development
expenditure of firm i in year t divided by the start-of-year book assets. As indicated in Table 1, the R&D
intensity averages at about 6.6% and exhibits a wide variation, with a standard deviation of 11.0%. R&D
output is measured as new product sales divided by total sales. Its mean value is 22.2%, with a standard
deviation of 34.7%.17
To deal with outliers, we winsorize some of the variables at the one percent level at both tails of the
distribution. These variables include: informal financing ratio, R&D intensity, and other firm-level
control variables such as sales growth, intangible to total assets ratio, return on assets (ROA), and
leverage. Detailed description of these firm-level variables can be found in Appendix 3.
3.4 Provincial institutional indices
To ensure that the impact of the IP rights enforcement is not due to other sources of provincial
differences, we augment our firm-level data set with information on provincial environment. We obtain a
measure of economic development, computed as the natural logarithm of GDP per capita in each province.
16Our sample in calculating the percentage of bank loan only includes the companies raising debts (the new debt is positive). It will also make the percentage of bank loan seem higher. 1782 observations are coded as missing record, because they have zero total sales at the date of observation.
17
Provincial GDP per capita is released by the National Bureau of Statistics of China (NBS). There is a
wide variation in the provincial GDP per capita, ranging from about $400 (in U.S dollars) to $7,000 with
a mean of $1,600 and a standard deviation of $1,200.
We also include three provincial institutional indices collected by the National Economic Research
Institution (NERI). 18 The first is the provincial index of banking system development (denoted as
“Banking” from now on), which is based on two dimensions: the competition in the financial industry
measured as the percentage of deposits taken by non-state owned financial institutions, and the transition
to free economy in loan allocation measured by the percentage of short-term loans to the firms in the non-
state sectors. A larger value of “Banking” implies a better developed banking system. The cross-province
variation of this index is substantial. It ranges from 0.85 to 11.48 with a standard deviation of 2.24. The
second provincial index is on the legal system performance (denoted as “Law” hereafter), which is
obtained from an annual survey of a representative sample of enterprises in each province regarding the
legal environment and judicial efficiency in protecting lawful business activities. The cross-province
variation of “Law” is also high. The lowest value of this index is 0.06, while the highest value is 10. The
third index is of provincial government corruption control (denoted as “Corruption Control” thereafter). It
is based on two components: the extent the local government intervening in businesses, measured as the
time spent by businesses in dealing with bureaucracy, and the level of non-tax expenses levied on
enterprises, including informal charges, any forms of apportionment, and illegal fines from the local
government, as a percentage of sales. A higher value of “Corruption Control” suggests a lower level of
provincial government corruption. “Corruption Control” varies from 1.77 to 13.68, with a standard
deviation of 2.40.
We link the provincial GDP per capital and the three provincial-level indices with the corresponding
18NERI indices are constructed by Fan and Wang (2006). They are widely used by economists and other social scientists in studying institutions of China (e.g., Wang et al., 2008). The NERI indices capture the process of market and institutional transition of 31 provinces or special districts in mainland China, including the following five dimensions: the relation between government and markets, the development of non-state sectors, the development of product market, the development of production elements markets, and the development of market intermediaries and legal environments.
18
firm-level data, by the provincial locations of the firms. There are 27 provinces and four special districts
(Beijing, Tianjin, Shanghai and Chongqing) in China. However, three relatively under-developed western
provinces (Ningxia, Qinghai and Tibet) do not report high tech companies in our sample. Thus, the
effective total number of provinces/districts in our analysis is 28.
Panel B of Table 1 reports the cross-correlation between the two measures of IP rights enforcement
and the measures of provincial institutions. As expected, the measures of IP rights enforcement are
positively correlated with GDP per capita and the three provincial level indices. The positive correlations
support that economic growth goes hand in hand with better institutions.
4. Empirical Analysis and Results
We report our empirical analyses and results in four subsections. We examine the impact of IP rights
enforcement on financing, R&D input, and R&D output, and we plot the time series of provincial level IP
rights enforcement. The evidence suggests that financing and investing in R&D are the channels in which
better IP rights enforcement could affect, on the aggregate, the growth of the economy.
4.1 Effect of IP rights enforcement on financing
4.1.1 Access to new debt
We examine the relationship between IP rights enforcement in a region and the individual firms’
ability to access external debt. We use a random effect logistic regression model for our panel data of
3,245 firms for four years, or 12,980 firm years. To control for other factors that may affect these firms’
ability to obtain external debt, we include both firm characteristics and regional differences in
institutional and economic development. The model is specified as the following:
i,t 1 i,t -1 2 i,t -1 3 i,t -1
4 i 5 i i,t
Y = α + β IP Rights Enforcement + β Firm Variables + β Provincial Variables + β Regional Dummies + β Industry Dummies + ε
(1)
The dependent variable, tiY , , is one if the net increase in debt is at least 5% of total assets in year t, and
zero otherwise. We use our two measures of IP rights enforcement, IPP1 and IPP2, in separate
estimations of the model. The set of firm level variables include: patent dummy (which is coded as 1 if
19
the company has any patents before the current year and 0 otherwise), R&D intensity, sales growth rate,
intangible to total assets, return on assets (ROA), leverage, natural logarithm of total assets, and natural
logarithm of firm age. The definitions of these firm characteristics are listed in Appendix 3. We also
adjust for provincial differences in the availability of financing for firms, including provincial indices of
banking system development, legal system performance, and control of local corruption, as well as
provincial GDP per capita. All independent variables in the regression model are lagged by one year in
order to avoid problems of endogeneity. Finally, we add industry dummies, and regional dummy variables
to represent five major regions of China: Northeast, Costal, Central, Northwest, and Southwest (detailed
definition is in Appendix 3) to capture unspecified regional effects.
Insert Table 2
Table 2 (Column 1 to 4) reports the logistics estimates of the marginal effect of IP rights enforcement
on the probability that a high tech company has access to new debt. Column 1 and 3 of Table 2 show that
both measures of IP rights enforcement significantly increase the probability of the firm access to new
debt. Moving from the lowest IPP1 province to the highest IPP1 province increases the probability of a
high tech firm obtaining new debt by 4%, holding all other independent variables at their mean values
(Column 1). Moving from the lowest IPP2 province to the highest IPP2 province is associated with a
6.7% increase in the probability of obtaining new debt, holding all other independent variables at their
mean values (Column 3). When the provincial institution indices are included (Column 2 and Column 4),
the probability increases by more than 6% (11%), respectively. Moreover, when we use the bootstrap
method to estimate the standard errors for the estimated parameters, our results still hold and the
estimated coefficients of the IP rights enforcement variables are even more significant.
To rule out the possibility that our measures of IP rights enforcement merely capture other regional
factors, we control for regional differences in banking developments, legal systems performance, and
control of local corruption, in Column 2 and Column 4. The effect of banking development on economic
growth has been previously investigated (King and Levine, 1993; Levine and Zervos, 1998; Beck, Levine
and Loayza, 2000). The evidence from cross-country comparisons suggests that the banking development
20
has a positive effect on GDP growth. Based on cross-province evidence in Italy, Benfratello, Schiantarelli
and Sembenelli (2006) suggest that, with better banking development, more credit is supplied to support
firms’ innovative activities. Consistent with these studies, the banking system development index has a
positive and statistically significant effect on the probability of a firm’s access to new debt. Nevertheless,
the effect of IP rights enforcement still holds after controlling for the banking development.
La Porta et al. (1997) find, in countries with poor legal protection for investors, both equity and debt
markets are smaller and narrower in scope. Demirguc-Kunt and Maksimovic (1998) show that firms in
the countries with better legal systems are more likely to obtain long-term external funds and grow faster,
because an effective legal system can help to uphold debt contracts, enforce covenants, deter potential
violation, and assess compensation in cases of infractions. After controlling for the legal system
performance index, IP rights enforcement still has a positive effect on the probability of firm access to
new debt.19 The coefficient of legal system performance index, however, is positive but not statistically
significant.
The provincial corruption control index (a high index value means lower corruption) is significantly
and positively correlated with both measures of IP rights enforcement, but the impact of IP rights
enforcement is still robust when we include the corruption control index. We find that firms in provinces
with a higher level of corruption have a significantly higher probability of obtaining new debt (the sum of
bank loan and informal financing). The reason is that firms in provinces with more severe corruption are
more likely to obtain loans from government owned banks, whereas the level of corruption has no impact
on informal financing across provinces. In the cross-country studies, La Porta, Lopez-de-Silanes, Shleifer,
and Vishny (2002), Fan, Titman, and Twite (2006), and Sapienza (2004) provides evidence supporting
that corrupt bureaucrats channel funds to their favored firms through the banking system, and government
owned banks mostly favor firms located in depressed area. Khwaja and Mian (2005) and Fan, Rui, and
19The correlation between our law-enforcement efficiency index and our measure of intellectual property protection is 29 percent. This correlation might generate the suspicion that the enforcement effect we are observing is due to the role of efficient legal system in general. The results above show that the enforcement effect plays a more important role in the financing of high tech firms.
21
Zhao (2008) also suggest that corruption gives the politically connected companies greater access to debt
from the government owned banks.
Following a few cross-province studies (Guiso, Sapienza, and Zingles, 2004; Cull and Xu, 2005), we
include provincial GDP per capita in the regression to control for the provincial level wealth. The level of
GDP per capita has a negative and statistically significant effect on the probability of firm access to new
debt. This result is consistent with the cross-country finding of Demirguc-Kunt and Maksimovic (1998),
who report that in richer countries, a lower proportion of firms rely on external financing to fund growth.
We also include the one-year lagged growth rate of provincial GDP per capita in a separate regression,
and the estimated effect of IP rights enforcement remains unchanged (not tabulated). All other firm-level
control variables have the expected signs: holding patents, higher R&D intensity, larger sales growth, and
higher profitability increase the probability of firm access to new debt, while intangible to total assets
ratio, leverage, and firm age have the opposite effect.
4.1.2 Informal financing ratio
The use of informal financing is not well understood, yet it counts for the bulk of short term
financing by firms. We investigate the determinants of informal financing. To facilitate comparison, we
use the same specification as in Equation (1) to estimate the effects of IP rights enforcement on the ratio
of informal financing in total new debt. The only difference is that we use a random-effects linear
regression model for the panel data, since the dependent variable is now a continuous variable. The
reported standard errors are robust to heteroskedasticity and within-firm residual correlations.
Column 5 of Table 2 shows that IP rights enforcement, measured by IPP1, increases informal
financing ratio, as predicted in Hypothesis 2. This effect is statistically significant at the one percent level.
A one-standard-deviation increase in IPP1 raises the informal financing ratio by three percentage points.
Moving from lowest-IPP1 province to the highest-IPP1 province increases the informal financing ratio by
18 percentage points, about a fourth of the sample mean. The level of GDP per capita has a positive and
significant effect on the informal financing ratio. Other firm-level control variables are statistically
significant: intangible to total assets ratio, leverage, and firm size reduce the fraction of informal
22
financing in new debt.
After controlling for the banking development index, the legal system performance index, and the
corruption control index in Column 6, IP rights enforcement still has a positive and statistically significant
effect on the proportion of informal financing in new debt. The coefficient of IP rights enforcement is
even greater than that in Column 5, suggesting that better enforcement of IP rights for high tech
companies and better institutions are complementary. The development of banking system has a negative
but statistically insignificant effect on the informal financing ratio. The performance of the legal system
has a negative and statistically significant effect on informal financing ratio. These results suggest that a
well developed banking system and an efficient legal system enhance the probability a company’s access
to bank loan, leading to more reliance on banks and less so on informal financing. The level of
government corruption control index has no effect on informal financing; but it has a negative effect on
bank financing as China’s banking system is dominated by government owned banks and corruption cases
occur frequently in China’s banking system, as shown by Fan, Rui, and Zhao (2008). The net result is a
lower informal financing ratio in the regions with greater corruption. When measured by the
technological market size (Column 7 and 8 of Table 2), IP rights enforcement again has a positive and
statistically significant effect on informal financing ratio. A one-standard-deviation increase in the
technological market size raises the informal financing ratio by 2.2 percentage points.
4.1.3 Access to new external equity
Column 9 in Table 2 reports the logistic estimate of the marginal effect of IP rights enforcement on
the probability of firm access to new external equity. As predicted, the effect is positive and statistically
significant. It’s also economically meaningful. Moving from the lowest-IPP1 province to the highest-IPP1
province leads to an increase of 4 percentage points in the probability of firm access to new external
equity, almost a fourth of the average probability of obtaining new external equity in the sample.
This result still holds when we control for provincial institution indices (Column 10), or when we use
technological market size as the measure of IP rights enforcement (Column 11 and 12). Legal system
performance has a positive and statistically significant effect on the probability of firm access to new
23
external equity. The coefficient of GDP per capita is negative and significant. These results are also
consistent with the cross-country findings of Demirguc-Kunt and Maksimovic (1998).
All other control variables have the expected signs and several of them are statistically significant. A
higher sales growth rate enables companies to raise more external equity. Companies with more
intangible assets, lower profitability, and higher leverage are less likely to have access to new debt
(Column 1 to 4), so they have to rely more on new equity, which are essentially funds from existing
shareholders and parent companies.
The results in this sub-section support Hypothesis 1 and Hypothesis 2: high tech firms in regions
with better IP rights enforcement have greater access to external financing (new debt and new external
equity), and they are also more able to seek financing from informal sources.
4.2 R&D input
We estimate the effect of IP rights enforcement on firms’ investment in R&D. We hypothesize that
firms in provinces with better IP rights enforcement are more inclined to invest in R&D, and thus are
more willing to allocate greater shares of funds raised to R&D. The random-effects linear regression
model takes the following form:
i,t 1 i,t 2 i,t i,t-1
3 i,t-1 4 i,t-1
R & D Intensity = α + β Sources of Funds + β (Sources of Funds IP Right Enforcement ) + β IP Rights Enforcement + β Provincial Variables
×
5 i 6 i i,t + β Regional Dummies + β Industry Dummies + ε
(2)
We define the dependent variable, R&D Intensity, as R&D investment by firm i in year t divided by its
total assets at the beginning of the year. R&D intensity is estimated as a function of IP rights enforcement,
the newly raised funds from three sources (debt, external equity and internal financing), and the
interactions of the funding sources with measures of IP rights enforcement. The funding sources are
scaled by total assets (the detailed description is given in Appendix 3). Also included are provincial
institution indices, regional dummies, and industry dummies. All independent variables in the regression
model, except for the three new funding variables, are lagged by one year to avoid the problems of
endogeneity. The standard errors are robust to heteroskedasticity and within-firm residual correlations.
24
Insert Table 3
The coefficients and t-stat obtained from the estimation of Equation (2) are presented in Column (1)-
(2) in Table 3. The interaction terms between the IP rights enforcement and funding from new debt, new
equity and internal financing are positive. The results suggest that in provinces with better IP rights
enforcement, high tech companies will invest a significantly higher proportion of new debt and new
internal finance in R&D, and also a higher but statistically insignificant proportion of external equity in
R&D. When moving from the lowest-IPP1 province to highest-IPP1 province, the percentage of new debt
invested in R&D doubles from 3% to 6%, while the percentage of new internal finance invested in R&D
rises from 9.5% to 14%.
The results above are robust to controlling for provincial institution indices (Column 2). They also
hold with the alternative measure of IP rights enforcement, although the effect of internal financing on
R&D intensity becomes less significant (Column 3 and 4). The three institution indices could affect a
firm’s willingness to invest newly raised fund in R&D. Therefore, we re-estimate the basic model in
Column 1 by controlling for the interactions between the institution indices and the three funding sources.
The estimated coefficients of the interaction terms between IP rights enforcement and new debt, and
between IP rights enforcement and new internal finance, remain positive and statistically significant (not
tabulated).
R&D intensity and funding sources may be simultaneously determined, and R&D expenditure could
have impact on the new funding. To tackle this issue, we estimate a simultaneous-equations model
specified as follows:
t t -1
t-1
t
t t R & D Intensity = f(Three Funding Sources , Three Funding Sources IP Rights Enforcement , IP Rights Enforcement , Control Variables) New Debt/Asset = f(R & D Intensity
×
t -1 t-1
t t-1 t-1
t t-1
, IP Rights Enforcement , Control Variables) New External Equity/Asset = f(R & D Intensity , IP Rights Enforcement , Control Variables) New Internal Equity/Asset = f(R & D Intensity , IP Rig t-1hts Enforcement , Control Variables)
⎧⎪⎪⎪⎨⎪⎪⎪⎩
(3)
The estimated results are presented in Column (5)-(6) in Table 3. They are consistent with what we
25
have observed in the random-effects panel data regression: in provinces with better IP rights enforcement,
high tech companies will invest a significantly higher proportion of new debt in R&D. Table 3 provides
evidence supporting Hypothesis 3: better IP rights enforcement increases the investment in R&D by high
tech firms.
4.3 R&D output
We now explore the impact of IP rights enforcement on R&D output. After all, the ultimate
objective of firms, in seeking better protection of intellectual assets, is to create and sell more new
products at a profit. First, we measure the R&D output of a firm as the count of each type of patents
(invention patents, utility model patents, and design patents), and the total number of all three types. The
number of patents should be affected by IP rights enforcement: firms in regions with better IP rights
enforcement receive greater protection from patent infringement, and therefore they are more likely to
seek patent generation, registration, and application.
According to Hausman, Hall and Griliches (1984, 1986), and Crepon and Duguet (1997), the proper
methodology of dealing with a discrete non-negative dependent variable is the Poisson regression model.
The number of patents created by firm i at year t, Pit, is assumed to be independent and has a Poisson
distribution with the parameter λit. λit depends on a set of explanatory variables which are the
determinants of patent creation. The model is as follows:
i,t 0 1 i,t 2 i,t-1 2 i,t-1
3 i,t-1 4 5 i,t
ln(λ )= β + β ln(R & D Capital Stock) + β ln(IP Rights Enforcement) + β ln(Total Asset) + β ln(Age) + β Industry Dummies + β Regional dummies +ε
(4)
We choose this log-linear relationship because it allows interpretation of 2β as the elasticity of the
mean patent number with respect to the enforcement of IP rights. R&D capital stock is computed from
the standard exponentially declining formula for capital stocks, , , 1 ,(1 )i t i t i tk k rδ −= − + , where ,i tk is the
end-of-period stock of R&D capital and ,i tr is the R&D expenditure during the year t (Crepon and Duguet,
1997; and Hall, Jaffe, and Trajtenberg, 2005). The depreciation rate δ is set to be 15%, which is
26
generally adopted by prior literature.20
Insert Table 4
Panel A of Table 4 shows that, in the Poisson regression model, IP rights enforcement has a positive
and significant impact on the number of innovation patents and total patents. R&D stock has a positive
and highly significant effect on the number of each class of patents and the total number of all types of
patents. Large firms have more patents than small firms. And older firms tend to generate more design
patents. The results are consistent with the hypothesis that a strong IP rights enforcement encourages the
producing of patents.
Our second measure of R&D output is the ratio of new product sales. Poor IP rights enforcement
could adversely affect new product introduction and subsequent sales figures. We use a random-effects
linear regression model to estimate the effect of IP rights enforcement on new product sales.
i,t 1 i,t-1 2 i,t-1
3 i,t-1 4 i 5 i i,t
New Product Sale Ratio = α+ β IP Rights Enforcement + β Provincial Variables + β Firm Variables + β Regional Dummies + β Industry Dummies + ε
(5)
The first column in Panel B of Table 4 shows that even after controlling for the R&D input variable
(R&D intensity), the level of IP rights enforcement, IPP1, still has a positive and highly significant effect
on new product sales ratio. A one-standard-deviation increase in the level of IP rights enforcement
improves the new product sales ratio by 5.4 percentage points, about one fourth of the sample mean. This
effect is still highly significant when controlling for provincial institution indices (Column 2). However,
the magnitude is somewhat reduced: a one-standard-deviation increase in the IP rights enforcement is
associated with an increase of 3.8 percentage points in the new product sales ratio. It suggests that better
institutions could facilitate new product sales, thus reducing the reliance on IP rights enforcement. Our
results are also robust to different definitions of IP rights enforcement (Column 3 and 4). We also use
different specifications of R&D input by substituting the average 3 year R&D intensity for the one year
lagged R&D intensity, and the results still hold (not tabulated).
20The choice makes little difference to our result, because our sample period is only five years and we choose to include only two lagged R&D expenditures.
27
The results in Table 4 are consistent with Hypothesis 4A and Hypothesis 4B: better IP rights
enforcement increases patents obtained by firms, and it increases firms’ sales from new products.
4.4 Time series of provincial level IP rights enforcement
Figure 3A plots the IP rights enforcement as measured by the density of property rights law offices
from 2002-2006 (IPP1). We divide the regions into two groups based on the initial value of the measure
in 2002. The regions with above-the-median IPP1 in 2002 keep improving the enforcement, while the
regions with below-the-median IPP1 in 2002 do not improve the enforcement over time. A similar picture
emerges if we measure the IP rights enforcement as technical market size (Figure 3B).
Insert Figure 3
Thus, we have a dichotomy: regions that enforce intellectual property protection relatively well
receive benefits from the enforcement in the forms of more investments in R&D and greater R&D output
(thus greater economic growth), which in turn give them the incentive to further improve their
enforcement of IP rights. On the other hand, those with poor IP rights enforcement at the beginning
receive very little benefit as the result of the poor enforcement, and they do not seem to perceive the need
to improve their enforcement of IP rights.
5. Further Analysis
In this section, we extend our analysis by dealing with two issues that could affect our results. We
discuss the two issues and perform additional analysis.
5.1 Reclassifying high tech firms
The first is a data issue. Although our companies are located in special zones for high tech firms
where they could enjoy certain tax breaks and other subsidies, we do find some firms do not seem to be
actively engaged in R&D. It may be possible that these firms surreptitiously gain admission to the special
high tech zones to enjoy the benefits, or they simply desire to be branded as high tech. If this is true, we
would have misclassified some companies as high tech.
28
We decide to divide the sample into intellectual property intensive versus non intensive subsets using
three different methods of classification. First, we consider a company as intellectual property intensive
if it owns at least one patent before the current year (patent dummy). We find only one out of five firms
qualify as an intellectual property intensive company. Second, we classify companies as intellectual
property intensive if they introduced new products in the past year, i.e., positive new product sales. This
measure (new product dummy) results in 41% of observations being classified as intellectual property
intensive companies. 21 The third measure (high R&D dummy) is whether the R&D intensity of a
company in the previous year is higher than the median of its industry R&D intensity.
After classifying the companies into intellectual property intensive versus non intensive subsets, we
are able to estimate the impact of IP rights enforcement on each subset.22 We re-estimate the basic
specifications of Table 2 and Table 4, and present the results in Table 5. As expected, the first three
columns (access to new debt) show that IP rights enforcement matters only for intellectual property
intensive firms. The IPP1 coefficients for intellectual property intensive firms are uniformly positive and
statistically significant, while they are not significant for non intellectual property intensive companies.
Insert Table 5
Columns 4 to 6 of Table 5 report the estimates of the effect of IP rights enforcement on informal
financing ratio for intellectual property intensive and non intensive companies. Columns 4 and 5 show
that the effect of the IP rights enforcement on informal financing ratio for intellectual property intensive
companies is 1.3 to 1.7 times as large as that for the non intensive companies. The difference is
statistically significant at the one percent level when intellectual property intensity is measured as the new
product dummy. However, when using the high R&D dummy as the measure of the intellectual property
intensity, we do not find a larger impact of IP rights enforcement on informal financing ratio among 21The discrepancy between the number of firms with patents and those with new products could due to how new products are defined. Some firms may consider products using foreign patents, or products not yet introduced into China, as new products. Those firms may not be performing innovative research, but they would still need IP rights enforcement. 22Because the basic specification of Table 2 and Table 4 includes the patent holding dummy, all the regressions in Table 5 include this variable. When measuring IP-intensity using the high R&D dummy, we exclude the R&D intensity variable because the bulk of the information in this variable has been captured by the high R&D dummy, although including the R&D intensity does not change our results.
29
intellectual property intensive companies. It is possibly due to the relative nature of this classification
scheme. 23
The effect of IP rights enforcement on firm access to external equity (Table 5, Column 7 to 9) is
significant in both sub samples. The last three columns in Table 5 compare the effects of IP rights
enforcement on new product sales ratio between intellectual property intensive and non intensive
companies. Again, we find that the effect of IP rights enforcement on new product sales ratio for
intellectual property intensive companies is 1.3 to 2.7 times as large as for non intensive companies. The
difference is statistically significant at the one percent level regardless of which measure of intellectual
property intensity is used.
5.2 Agency costs and ownership types
As discussed earlier, IP rights enforcement matters because it helps to solve the problem of
appropriation by competitors, the information asymmetry, and the agency problem in joint ventures. In
the Section 5.1, we show that IP rights enforcement matters more for intellectual property intensive firms.
Those companies are more likely to encounter the problems of appropriation by competitors and
information asymmetry.
In this section, we continue to explore how IP rights enforcement matters by focusing on its role in
solving the agency problem in joint ventures. The ownership-type information in our unique database
enables us to perform this task. We re-estimate the basic specifications in Table 2 and 4 for companies of
eight ownership types. Although we focus on joint ventures, firms of other ownership types are included
as comparisons. The results are presented in Table 6. Column 1 shows that the probability of firms having
access to new debt in foreign joint ventures is lower than in foreign solely owned companies by 10
percentage points. This effect is statistically significant at the one percent level. However, stronger IP
rights enforcement increases the probability of obtaining new debt for foreign joint ventures. One
23This measure using high R&D dummy is relative and could conceivably classify a low (absolute) R&D firm in a low R&D industry as high (relative) R&D, and conversely, classify a high (absolute) R&D firm in a high R&D industry as low (relative) R&D.
30
standard deviation increase in the enforcement raises the probability by 2 percentage points. We also find
a positive result for ethnic joint ventures, although the effect is statistically insignificant at the
conventional level. Column 2 and 3 show that foreign joint ventures and ethnic joint ventures have a
lower probability to obtain external equity and informal financing than solely owned companies; however,
better IP rights enforcement mitigates this adverse effect. In addition, in Column 4, we find that foreign
(ethnic) joint ventures produce slightly fewer innovation patents than foreign (ethnic) solely owned
companies, but better IP rights enforcement markedly stimulates joint ventures to create more innovation
patents. These results in Table 6 for joint ventures and foreign solely owned firms demonstrate the role of
better IP rights enforcement in mitigating agency problem between the foreign (ethnic) partners and the
local partners, consistent with Hypothesis 5.
Insert Table 6
Although joint ventures are less likely to have access to external financing, we find that they have
higher new products sales ratio than solely owned companies (Column 5). This result is not surprising,
because foreign and ethnic solely owned companies usually introduce older, existing products from
abroad to China, which are then counted as new products inside China. However, we still find that better
IP rights enforcement increases new product sales for foreign joint ventures and ethnic joint ventures.
Our earlier discussion suggests that, because of the possibility of appropriation of technology by
local partners, the more technologically advanced foreign partners are reluctant to transfer technology to
the joint venture companies and may also be discouraged to make local R&D investments. We find
evidences supporting this conjecture. We compare the difference in the mean value of R&D intensity
between joint ventures and foreign solely owned companies. The average R&D intensity of foreign joint
ventures and foreign solely owned companies are 0.06 and 0.09, respectively. The difference is
statistically significant at the one percent level. The corresponding numbers for ethnic joint ventures and
ethnic solely owned companies are 0.05 and 0.08, also significant at the one percent level. To examine
whether IP rights enforcement plays a role in promoting R&D investment of joint ventures, we re-
estimate the basic regression in Table 3 on the sample of joint ventures. We find that better IP rights
31
enforcement has a positive and statistically significant effect on the R&D intensity of joint ventures (not
tabulated).
Table 6 also allows us to address the difference in the importance of IP rights enforcement for
various ownership types. We find, for every ownership type, IP rights enforcement helps the companies to
obtain new external financing, produce more innovation patents, and sell more new products. In 25 out of
40 cases, these effects are statistically significant at the conventional level. The lack of significance for
some cases might be due to their small sample size, e.g., there are only 60 collective-owned companies.
Another reason is that some types of companies are less sensitive to IP rights enforcement. State-owned
firms are less sensitive to the provincial level enforcement of IP rights in obtaining debt as they have
access to state-owned banks. Foreign-owned enterprises need strong IP rights enforcement if they are to
secure new equity funds from their parent companies, i.e., greater commitment by parents. We find that
better IP rights enforcement enables enterprises owned by ethnic Chinese (those from Hong Kong,
Taiwan, and Macau) to secure all types of debt including those from informal sources. Finally, as
expected, we find IP rights enforcement is essential to protect new products for all ownership types.
6. Conclusions
Our study of Chinese high tech firms shows that IP rights enforcement matters. High tech firms in
provinces with better IP rights enforcement have greater access to external debt (formal and informal) and
new equity. They are also more willing to invest in R&D, and have better tangible results, i.e., more
patents and new product sales. We also show that better IP rights enforcement mitigates the agency costs
in joint ventures, by reducing the risk of appropriation by local partners. Consequently, joint ventures in
regions with better IP rights enforcement secure more external financing and invest in more R&D.
Now we have an answer to the question raised on the China puzzle at the beginning of the paper: is
China so different that it could have economic and R&D growth without regard to the protection of
intellectual property? By examining different degrees of IP rights enforcement in different provinces in
the same country, we find that protection of intellectual property does matter in China. Regions with
32
better IP rights enforcement achieve greater economic growth from high tech industries, and they, in turn,
further improve their enforcement of IP rights. Provinces that had poor enforcement at the beginning of
the sample period do not improve their protection of intellectual property over time, and they are laggard
in R&D investments and outputs.
This paper makes the connection between the enforcement of IP rights and economic growth via the
channels of financing of and investing in R&D. Our research design enables us to study the effect of the
local enforcement of IP rights, as the enforcement varies across regions. However, international
differences in the enforcement of these rights must await future efforts.
33
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37
Licenses Paid by Chinese Companies to Foreign Countries
0
5
10
15
20
25
30
2004 2005 2006 2007
Year
Total EU Japan US Others
Billion US$
Figure 1 Figure 1 shows the licensing fee paid by Chinese companies to foreign countries from 2004 to 2007. The data come from Ministry of Commerce of China.
38
Shanghai
河南
Beijing
Average density of intellectual property law firms in Chinese provinces from 2001 to 2005 (Number of IP law firms per ten thousand population)0.0025 to 0.0050.005 or more 0.0015 to 0.0025 0.001 to 0.0015
Figure 2 Intellectual Property Rights Enforcement across Chinese Provinces: Density of Intellectual Property Law Firms
39
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
2002 2003 2004 2005 2006
year
Den
sity
of I
P L
aw F
irm
s
Strong IP-Enforcement Provinces Poor IP-Enforcement Provinces
(A)
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Year
Tec
hnic
al M
arke
t Siz
e
Strong IP-Enforcement Provinces Poor IP-Enforcement Provinces
(B) Figure 3 Figure 3A plots the enforcement of intellectual property right as measured by the density of property right law offices from 2002-2006 (IPP1). We divide the regions into two groups based on the initial index value in 2002. Strong (poor) IP-enforcement provinces are those with the initial index value above (below) median. Figure 3B plots the enforcement of intellectual property right as measured by technical market size from 1997-2006 (IPP2). Regions are divided into two groups based on the initial index value in 1997. Strong (poor) IP-enforcement provinces are those with the initial index value above (below) median.
40
Table 1 Summary statistics and correlation matrix
Panel A contains summary statistics of provincial variables and firm-level characteristics. Panel B shows the correlation coefficients among the measures of IP rights enforcement and provincial-level institutional variables. Panel C summarizes the frequency of different ownership types. The number in parentheses is the significance level of each correlation coefficient. The detailed description of the variables is in Appendix 3.
Panel A: Summery Statistics
Mean S. D. Min 25th
percentile Median
75th percentile
Max Obs.
Provincial variables IPP1 0.007 0.015 0.001 0.002 0.002 0.005 0.089 140 IPP2 0.008 0.011 0 0.003 0.004 0.008 0.071 140 Banking 5.961 2.235 0.850 4.415 6.000 7.205 11.480 140 Law 4.264 2.265 0.060 2.655 4.265 5.610 10.000 140 Corrupt Control 7.098 2.399 1.765 5.395 6.928 8.673 13.675 140 GDP per Capita 9.261 0.608 7.971 8.798 9.153 9.597 10.921 140
Firm-level variables Access to New Debt 0.435 0.496 0 0 0 1 1 12980 Access to New External Equity 0.155 0.362 0 0 0 0 1 12980 Informal Financing Ratio 0.714 0.764 -2.348 0.765 1 1 2.424 2633 R&D Intensity 0.066 0.108 0 0.006 0.030 0.076 0.664 12980 New Product Sales Ratio 0.222 0.347 0 0 0 0.385 1 12867 Patent Dummy 0.213 0.410 0 0 0 0 1 12980 Sales Growth 0.480 1.723 -0.928 -0.128 0.122 0.447 11.100 16225 Intangible to Total Assets Ratio 0.043 0.085 0 0 0.002 0.049 0.480 16225 ROA 0.047 0.099 -0.246 0.0003 0.026 0.082 0.423 16225 Leverage 0.472 0.258 0.001 0.275 0.471 0.658 1.089 16225 New Debt/Asset 0.087 0.285 -0.557 -0.046 0.028 0.169 1.413 12980 New External Equity/Asset 0.022 0.198 -0.516 -0.015 0 0.008 1.110 12980 New Internal Finance/Asset 0.036 0.098 -0.221 0 0.007 0.058 0.482 12980 Number of innovation patents 0.724 27.198 0 0 0 0 2254 16225 Number of utility model patents 0.315 2.837 0 0 0 0 164 16225 Number of design patents 0.302 12.695 0 0 0 0 1101 16225 Number of total patents 1.342 31.690 0 0 0 0 2261 16225 Log (R&D stock) 8.242 2.446 0 7.361 8.586 9.653 14.803 9735 Log(Assets) 11.216 1.317 9.210 10.199 10.973 12.023 17.046 16225 Log(Firm Age) 2.012 0.670 0 1.609 2.079 2.398 4.454 16225
Panel B: Correlation matrix of IP rights enforcement and provincial-level institutional variables
IPP1 IPP2 GDP per Capita Banking Law Corruption
Control IPP1 1 IPP2 0.896 1 (0.000) GDP per Capita 0.537 0.524 1 (0.000) (0.000) Banking 0.140 0.202 0.668 1 (0.099) (0.017) (0.000) Law 0.288 0.291 0.713 0.526 1 (0.001) (0.001) (0.000) (0.000) Corruption Control 0.204 0.191 0.593 0.639 0.346 1
(0.016) (0.024) (0.000) (0.000) (0.000)
41
Panel C: The frequency of different ownership types
Ownership Type N Percentage
(%) Ownership Types of Foreign and Ethnic owned companies
N Percentage
(%) State-owned enterprises 3595 22.16% Foreign joint ventures 1814 58.23% Privately owned enterprises 4785 29.49% Foreign solely-owned 1301 41.77% Foreign owned enterprises 3115 19.20% Ethnic joint ventures 957 60.77% Ethnic Chinese owned enterprises 1575 9.71% Ethnic solely-owned 618 39.23% Collective owned enterprises 300 1.85% Others 2855 17.60% Total 16225 100%
42
Table 2 Effect of IP rights enforcement on access to external financing In Column (1)-(4), the dependent variable is an indicator that takes the value of one if there is a net increase of debt for firm i in a given year which exceeds 5% of its total assets, and zero otherwise; in Column (5)-(8), the dependent variable is the proportion of informal financing (debt minus bank loan) in the newly raised debt; in Column (9)-(12), the dependent variable is an indicator that takes the value of one if there is a net increase of external equity for firm i in a given year which exceeds 5% of its total assets, and zero otherwise. Only firms that raised new debt exceeding 5% of their total assets in 2004 and 2005 enter the regressions in Column (5)-(8), because the data of bank loan is available only in these two years. For a description of all the other variables see Appendix 3. All regressions include regional dummies and industry dummies as part of the control variables. All the independent variables are lagged by one year to avoid problems of endogeneity. For Column (1)-(4) and (9)-(12), the reported coefficients are logistic estimates of the effect of marginal change in the corresponding regressors on the probability of access to new debt or new external equity, computed at the sample mean of the independent variables. The coefficients reported in Column (5)-(8) are from a random-effects linear panel data model with standard errors robust to heteroskedasticity and within-firm residual correlation. ***, **, * indicate the coefficient is statistically different from zero at the 1-, 5-, and 10-percent level, respectively.
Access to New Debt Informal Financing ratio Access to New External Equity
Column (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) IPP1 0.461 0.729 2.002 3.107 0.424 0.648 (1.93)* (1.87)* (2.79)*** (2.42)** (2.76)*** (2.58)*** IPP2 0.936 1.582 2.012 1.917 0.403 0.571 (2.47)** (2.82)*** (1.98)** (1.69)* (1.66)* (1.67)* Banking 0.020 0.022 -0.016 -0.033 -0.001 -0.003 (3.35)*** (3.87)*** (0.81) (2.33)** (0.37) (1.04) Law -0.007 -0.005 -0.036 -0.049 0.008 0.007 (1.37) (1.11) (2.30)** (3.54)*** (2.83)*** (2.44)** Corruption Control -0.019 -0.019 0.131 0.127 0.001 0.0004 (4.57)*** (4.73)*** (8.28)*** (8.95)*** (0.45) (0.17)
GDP Per Capita -0.105 (5.49)***
-0.075 (2.41)**
-0.118 (5.64)***
-0.101 (3.19)***
-0.429 (5.49)***
0.165 (2.26)**
-0.065 (0.63)
0.200 (2.50)**
-0.055 (4.49)***
-0.078 (3.85)***
-0.048 (3.62)***
-0.052 (2.61)***
Patent dummy 0.050 (3.87)***
0.051 (3.99)***
0.050 (3.89)***
0.051 (4.00)***
0.202 (3.89)***
-0.019 (0.59)
-0.020 (0.61)
-0.021 (0.63)
0.005 (0.59)
0.006 (0.68)
0.004 (0.50)
0.002 (0.31)
R&D Intensity 0.323 (5.80)***
0.324 (5.81)***
0.325 (5.83)***
0.326 (5.85)***
1.320 (5.80)***
-0.152 (0.93)
-0.141 (0.87)
-0.149 (0.90)
0.044 (1.32)
0.044 (1.32)
0.043 (1.28)
0.049 (1.47)
Sales Growth 0.014 0.014 0.014 0.014 0.057 0.006 0.007 0.007 0.004 0.004 0.004 0.005 (6.09)*** (6.02)*** (5.69)*** (6.05)*** (6.09)*** (1.01) (1.21) (1.11) (3.26)*** (3.22)*** (3.36)*** (3.55)*** Intangible/TA -0.189 -0.160 -0.189 -0.195 -0.773 -0.345 -0.306 -0.358 0.132 0.131 0.132 0.128 (3.09)** (2.37)** (3.10)** (3.18)*** (3.09)*** (1.84)* (1.64) (1.89)* (3.75)*** (3.71)*** (3.72)*** (3.61)*** ROA 0.115 0.115 0.118 0.118 0.471 -0.021 0.035 -0.047 -0.064 -0.068 -0.065 -0.073 (2.23)** (2.23)** (2.28)** (2.28)** (2.23)** (0.14) (0.24) (0.31) (1.91)* (2.03)** (1.94)* (2.17)**
43
Leverage -0.116 -0.119 -0.116 -0.119 -0.475 -0.214 -0.213 -0.215 0.183 0.184 0.183 0.181 (4.85)*** (4.95)*** (4.85)*** (4.96)*** (4.84)*** (4.04)*** (4.07)*** (4.05)*** (13.98)*** (14.04)*** (13.94)*** (13.81)*** Log(assets) -0.001 -0.0004 -0.0003 -0.0004 -0.002 -0.052 -0.058 -0.055 -0.012 -0.012 -0.013 -0.012 (0.12) (0.11) (0.07) (0.08) (0.12) (4.58)*** (5.16)*** (4.91)*** (4.57)*** (4.57)*** (4.76)*** (4.58)*** Log(age) -0.037 -0.033 -0.037 -0.033 -0.153 0.052 0.026 0.043 -0.006 -0.005 -0.008 -0.008 (4.58)*** (4.00)*** (4.61)*** (4.03)*** (4.58)*** (1.84)* (0.92) (1.54) (1.30) (1.03) (1.54) (1.60) Observations 12980 12980 12980 12980 2633 2633 2633 2633 12980 12980 12980 12980 No. of firms 3245 3245 3245 3245 2055 2055 2055 2055 3245 3245 3245 3245 Wald Chi-square (p-value)
268.01 (0.000)
290.17 (0.000)
270.12 (0.000)
293.80 (0. 000)
2836.24 (0.000)
7307.75 (0.000)
2863.62 (0.000)
7095.97 (0.000)
270.66 (0.000)
278.23 (0.000)
267.00 (0.000)
274.35 (0.000)
44
Table 3 Effect of IP rights enforcement on R&D investment The dependent variable is research and development expenditure of firm i in a given year divided by start-of-the-year of total assets. For a description of all the other variables see Appendix 3. All regressions include regional dummies and industry dummies as control variables. All independent variables are lagged by one year to avoid problems of endogeneity, except the funding source variables. The coefficients reported in column (1)-(4) are from a random-effects linear panel data model with standard errors robust to heteroskedasticity and within-firm residual correlation. The coefficients reported in column (5) and (6) are from a simultaneous-equations model. ***, **, * indicate the coefficient is statistically different from zero at the 1-, 5-, and 10-percent level, respectively. Dependent variable R&D investment Estimation method
Panel Data Regression (Random-effects) Simultaneous-Equations
(3SLS) (1) (2) (3) (4) (5) (6) New Debt/Asset 0.031 0.032 0.031 0.033 0.345 0.329 (5.96)*** (6.18)*** (5.71)*** (6.02)*** (3.81)*** (3.20)*** New External Equity/Asset 0.026 0.027 0.027 0.028 0.209 0.219 (3.51)*** (3.60)*** (3.42)*** (3.51)*** (2.30)** (2.15)** New Internal Finance/Asset 0.095 0.100 0.095 0.103 0.188 0.199 (5.43)*** (5.69)*** (5.31)*** (5.71)*** (2.12)** (2.01)** IPP1×New Debt/Asset 0.284 0.283 2.243 (2.85)*** (2.84)*** (1.89)* IPP1×New External Equity/Asset 0.011 0.014 -0.170 (0.08) (0.11) (0.14) IPP1×New Internal Finance/Asset 0.561 0.545 0.150 (1.75)* (1.69)* (0.13) IPP2×New Debt/Asset 0.376 0.352 3.496 (2.51)** (2.35)** (1.68)* IPP2×New External Equity/Asset -0.012 -0.013 -0.574 (0.06) (0.07) (0.29) IPP2×New Internal Finance/Asset 0.752 0.644 -0.086 (1.61) (1.37) (0.05) IPP1 -0.026 -0.129 -0.150 (0.54) (1.30) (1.31) IPP2 -0.066 -0.002 -0.146 (1.04) (1.03) (0.79) GDP Per Capita 0.013 0.011 (1.63) (1.48) Law 0.000 0.001 (0.32) (0.68) Banking 0.000 0.001 (0.08) (0.63) Corruption Control 0.001 0.001 (0.84) (0.58) Constant 0.032 -0.094 0.033 -0.095 -0.009 -0.008 (3.10)*** (1.33) (3.20)*** (1.18) (0.38) (0.33) Observations 12980 12980 12980 12980 12980 12980 Number of firms 3245 3245 3245 3245 3245 3245 Wald Chi-square (p-value)
2317.37 (0.000)
2395.91 (0.000)
2320.51 (0.000)
2394.11 (0.000)
4417.72 (0.000)
4418.02 (0.000)
45
Table 4: Effect of IP rights enforcement on R&D output In Panel A, the dependent variable is the number of innovation patents (Column 1), utility model patents (Column 2), design patents (Column 3), and total patents (Column 4). In Panel B, the dependent variable is new product sales divided by total sales. For a detailed description of all the other variables see Appendix 3. In Panel A and Panel B, all regressions include industry dummies and regional dummies as control variables. All independent variables are lagged by one year to avoid problems of endogeneity, except the log (R&D Stock). The coefficients reported in Panel A are from a random-effects Poisson regression model for panel data. For Panel B, the coefficients reported are from a random-effects linear panel data model with standard errors robust to heteroskedasticity and within-firm residual correlation. ***, **, * indicate the coefficient is statistically different from zero at the 1-, 5-, and 10-percent level, respectively.
Panel A: Number of Patents Innovation Patents Utility Model Patents Design Patents Total Patents
Column (1) (2) (3) (4) Log(IPP1) 0.175 -0.092 -0.135 0.082 (2.36)** (1.64) (1.48) (1.65)* Log(R&D Stock) 0.150 0.083 0.056 0.074 (5.66)*** (3.91)*** (2.23)** (5.32)*** Log(Assets) 0.469 0.425 0.746 0.473 (9.37)*** (8.50)*** (9.96)*** (13.48)*** Log(Firm Age) -0.382 -0.033 0.456 -0.277 (4.31)*** (0.35) (2.38)** (4.09)*** Observations 9735 9735 9735 9735 Number of firms 3245 3245 3245 3245 Wald Chi-square (p-value)
392.25 (0.000)
285.92 (0.000)
273.64 (0.000)
542.40 (0.000)
Panel B: New Product Sales Ratio Column (1) (2) (3) (4) IPP1 3.612 2.562 (19.35)*** (8.05)*** IPP2 5.251 3.650 (20.05)*** (10.02)*** Banking -0.024 -0.022 (5.50)*** (5.52)*** Law 0.002 -0.004 (0.56) (1.26) Corruption Control 0.005 0.000 (2.14)** (0.10) Per capita. GDP 0.014 0.074 -0.021 0.078 (1.10) (3.13)*** (1.59) (3.83)*** R&D Intensity 0.067 0.070 0.074 0.076 (1.96)** (2.02)** (2.17)** (2.22)** Patent Dummy 0.058 0.060 0.057 0.061 (6.25)*** (6.47)*** (6.20)*** (6.62)*** Log(Assets) 0.002 0.003 0.000 0.003 (0.46) (0.76) (0.09) (0.75) Log(Firm Age) 0.028 0.031 0.016 0.025 (4.39)*** (4.61)*** (2.49)** (3.81)*** Observations 12867 12867 12867 12867 Number of firms 3238 3238 3238 3238 Wald Chi-square (p-value)
880.11 (0.000)
930.15 (0.000)
938.61 (0.000)
990.71 (0.000)
46
Table 5 The impact of IP rights enforcement on intellectual property intensive and non-intensive firms This table re-estimates the basic regression in Table 2 and Table 4 (Panel B). We include the interaction terms between IP rights enforcement and the dummy variable of whether the firm is intellectual property intensive or non-intensive. There are three way of indentifying intellectual property intensive companies: the companies holding patents, having new product sales or with R&D intensity higher than industry median. For a description of all the other variables see the Appendix 3. All regressions include regional dummies and industry dummies as controls. All independent variables are lagged by one year to avoid problems of endogeneity. For Column (1) to (3) and Column (7) to (9), the reported coefficients are logistic estimates of the effect of marginal change in the corresponding regressor on the probability of access to new debt or new external equity, computed at the sample mean of the independent variables. The coefficients reported in the remained columns are from a random-effects linear panel data model with standard errors robust to heteroskedasticity and within-firm residual correlation. Last three rows of the table present p value for Wald test of equality of coefficients. ***, **, * indicate the coefficient is statistically different from zero at the 1-, 5-, and 10-percent level, respectively.
Access to New Debt Informal Financing ratio Access to New External Equity New Product Sales/Total Sales Column
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
IPP1 for Patent holder 1.601 3.872 0.586 3.634 (3.33)*** (2.72)*** (1.90)* (9.41)*** IPP1 for Non Patent 0.540 2.878 0.661 2.325 (1.37) (2.24)** (2.60)*** (7.27)*** IPP1 for New Product Developer
0.825
(1.99)**
4.061 (3.04)***
0.693 (2.59)***
2.600
(8.50)***
IPP1 for Non New Product 0.533 2.322 0.721 0.965 (1.28) (1.74)* (2.70)*** (3.43)*** IPP1 for High R&D 0.993 3.132 0.669 2.819 (2.44)** (2.39)** (2.54)** (8.68)*** IPP1 for Low R&D 0.275 3.170 0.611 2.230 (0.66) (2.35)** (2.30)** (6.85)*** High R&D Dummy 0.036 -0.008 -0.002 0.015 (2.73)*** (0.17) (0.20) (2.27)** New Product Dummy 0.001 -0.107 -0.013 0.129 (0.05) (2.17)** (1.46) (14.30)*** Patent Dummy 0.020 0.050 0.049 -0.051 -0.016 -0.020 0.008 0.007 0.006 0.022 0.053 0.059 (1.24) (3.86)*** (3.79)*** (1.04) (0.49) (0.61) (0.75) (0.88) (0.76) (1.98)** (6.21)*** (6.39)*** Banking 0.020 0.020 0.020 -0.016 -0.018 -0.016 -0.001 -0.002 -0.001 -0.023 -0.023 -0.024 (3.42)*** (3.36)*** (4.70)*** (0.78) (0.91) (0.81) (0.38) (0.50) (0.37) (5.37)*** (5.50)*** (5.62)*** Law -0.007 -0.007 -0.008 -0.035 -0.035 -0.036 0.008 0.009 0.009 0.001 -0.001 0.001 (1.39) (1.38) (1.56) (2.26)** (2.27)** (2.30)** (2.83)*** (2.92)*** (2.83)*** (0.39) (0.18) (0.42) Corruption Control -0.019 -0.019 -0.019 0.131 0.131 0.131 0.001 0.001 0.001 0.005 0.006 0.005 (4.58)*** (4.60)*** (4.70)*** (8.26)*** (8.28)*** (8.28)*** (0.45) (0.50) (0.45) (2.22)** (2.56)** (2.14)**
47
Per capita. GDP -0.076 -0.074 -0.069 -0.068 -0.052 -0.068 -0.078 -0.078 -0.077 0.072 0.077 0.075 (2.45)** (2.38)** (2.21)** (0.66) (0.50) (0.66) (3.85)*** (3.89)*** (3.81)*** (3.07)*** (3.75)*** (3.18)*** R&D Intensity 0.326 0.319 -0.134 -0.143 0.044 0.050 0.072 0.047 (5.85)*** (5.71)*** (0.82) (0.87) (1.31) (1.47) (2.09)** (1.41) Sales Growth 0.014 0.014 0.014 0.008 0.008 0.007 0.004 0.004 0.004 (6.10)*** (6.14)*** (6.26)*** (1.26) (1.30) (1.21) (3.21)*** (3.05)*** (3.21)*** Intangible/TA -0.197 -0.194 -0.187 -0.318 -0.315 -0.303 0.131 0.132 0.131 (3.23)** (3.17)*** (3.06)*** (1.70)* (1.68)* (1.63) (3.71)*** (3.73)*** (3.70)*** ROA 0.115 0.110 0.101 0.030 0.034 0.030 -0.068 -0.064 -0.064 (2.24)** (2.14)** (1.95)* (0.20) (0.23) (0.20) (2.03)** (1.92)* (1.91)* Leverage -0.119 -0.120 -0.119 -0.214 -0.209 -0.214 0.184 0.185 0.184 (4.96)*** (4.99)*** (4.97)*** (4.10)*** (4.01)*** (4.08)*** (14.04)*** (14.08)*** (14.00)*** Log(Assets) -0.0004 0.0004 -0.0004 -0.058 -0.055 -0.057 -0.012 -0.012 -0.013 0.003 0.001 0.004 (0.92) (0.09) (0.09) (5.13)*** (4.82)*** (5.06)*** (4.57)*** (4.41)*** (4.77)*** (0.78) (0.50) (1.20) Log(Firm Age) -0.032 -0.034 -0.034 0.027 0.028 0.026 -0.005 -0.004 -0.005 0.030 0.008 0.029 (3.92)*** (4.05)*** (4.17)*** (0.95) (1.00) (0.93) (1.04) (0.85) (1.05) (4.52)*** (1.40) (4.37)*** Observations 12980 12980 12980 2633 2633 2633 12980 12980 12980 12867 12867 12867 Number of firms 3245 3245 3245 2055 2055 2055 3245 3245 3245 3238 3238 3238 Wald Chi-square (p-value)
298.75 (0.000)
292.04 (0.000)
300.35 (0.000)
7326.32 (0.000)
7318.32 (0.000)
7363.92 (0.000)
278.32 (0.000)
277.14 (0.000)
281.74 (0.000)
975.78 (0.000)
2071.34 (0.000)
1015.44 (0.000)
Wald: Patent vs. Non-patent (0.002) (0.159) (0.727) (0.000) Wald: New Prod. vs. Non-New Prod.
(0.289) (0.010) (0.870) (0.000)
Wald: High R&D vs. Low R&D
(0.006) (0.955) (0.723) (0.000)
48
Table 6 IP rights enforcement and the agency cost of joint ventures In the first four columns we re-estimate the basic regression in Table 2 and Table 4. We include the interaction terms between IP rights enforcement and the dummy variable of firm ownership type. For a detailed description of all the other variables, see Appendix 3. All regressions include regional dummies and industry dummies as control variables. All independent variables are lagged by one year to avoid problems of endogeneity. For Column 1 and Column 3, the reported coefficients are logistic estimates of the effect of marginal change in the corresponding regressors on the probability of access to new debt or new external equity, computed at the sample mean of the independent variables. The coefficients reported in Column 4 are from a random-effects Poisson regression model for panel data. In Column 2 & 5, the reported coefficients are from a random-effects linear panel data model with standard errors robust to heteroskedasticity and within-firm residual correlation. ***, **, * indicate the coefficient is statistically different from zero, respectively, at the 1-, 5-, and 10-percent level.
Specification (1) (2) (3) (4) (5)
Dependent Variable Access to New
Debt
Informal Financing
ratio
Access to New External Equity
Number of Innovation
Patents
New Product Sale/Total
Sale Foreign joint venture -0.096 -0.116 -0.049 -0.688 0.038 (3.41)*** (1.30) (3.30)*** (0.51) (1.66)* Ethnic joint venture -0.030 -0.294 -0.055 -1.003 0.061 (0.74) (2.47)** (3.29)*** (0.56) (1.78)* SOE 0.010 0.052 0.014 3.458 -0.022 (0.42) (0.68) (0.92) (3.49)*** (1.19) POE 0.010 -0.114 0.038 4.176 -0.030 (0.47) (1.54) (2.57)*** (4.30)*** (1.86)* Foreign 0.012 0.198 0.023 9.295 -0.079 (0.40) (2.29)** (1.13) (7.38)*** (3.69)*** Ethnic 0.007 0.068 0.050 5.821 -0.091 (0.19) (0.65) (1.83)* (4.09)*** (3.24)*** COE 0.037 -0.133 -0.029 4.005 -0.102 (0.71) (0.67) (1.04) (1.96)** (3.23)*** IPP1 for foreign joint venture 1.049 3.437 0.755 0.891 3.543 (1.80)* (2.23)** (1.96)* (4.02)*** (6.64)*** IPP1 for ethnic joint venture 0.810 6.296 0.751 0.473 2.871 (1.05) (3.34)*** (1.52) (1.65)* (3.71)*** IPP1 for SOE 0.489 3.332 0.616 -0.163 1.761 (1.07) (2.47)** (2.12)** (1.55) (4.64)*** IPP1 for POE 0.747 4.560 0.466 0.098 3.134 (1.67)* (3.16)*** (1.65)* (1.08) (8.49)*** IPP1 for foreign solely owned -0.868 1.634 1.190 1.208 2.724 (1.27) (1.12) (3.00)*** (6.38)*** (4.39)*** IPP1 for ethnic solely owned 1.981 3.583 0.302 0.155 3.903 (2.15)** (2.07)** (0.56) (0.63) (4.27)*** IPP1 for COE 0.020 5.929 0.299 0.264 3.097 (0.02) (2.25)** (0.42) (0.67) (3.34)*** IPP1 for Others 0.636 3.089 0.473 -0.294 1.535 (1.30) (2.08)** (1.51) (1.61) (3.76)*** Banking 0.021 -0.010 -0.002 -0.024 (3.51)*** (0.51) (0.46) (5.56)*** Law -0.007 -0.039 0.008 0.003 (1.35) (2.43)** (2.72)*** (0.85) Corruption Control -0.020 0.129 0.0002 0.005 (4.86)*** (8.19)*** (0.09) (2.33)** GDP per capita -0.068 -0.107 -0.069 0.069 (2.16)** (1.02) (3.41)*** (2.97)***
49
Patent Dummy 0.049 -0.005 0.001 0.057 (3.77)*** (0.16) (0.11) (6.07)*** R&D Intensity 0.333 -0.112 0.034 0.073 (5.95)*** (0.69) (0.99) (2.12)** Sales Growth 0.014 0.007 0.004 (6.03)*** (1.24) (3.35)*** Intangible/TA -0.189 -0.259 0.127 (3.08)** (1.35) (3.59)*** ROA 0.131 0.083 -0.075 (2.51)** (0.55) (2.24)*** Leverage -0.119 -0.196 0.181 (4.96)*** (3.74)*** (13.81)*** Log(Assets) 0.001 -0.073 -0.011 0.484 0.005 (0.18) (6.42)*** (3.89)*** (9.40)*** (1.39) Log(Firm Age) -0.032 0.024 -0.003 -0.425 0.031 (3.82)*** (0.84) (0.63) (4.66)*** (4.71)*** Log(R&D stock) 0.137 (5.19)*** Observations 12980 2633 12980 9735 12867 Number of firms 3245 2055 3245 3245 3238 Wald Chi-square (p-value)
320.33 (0.000)
7757.16 (0.000)
317.59 (0.000)
613.39 (0.000)
1031.97 (0.000)
50
Appendix 1: List of China’s Current Main Laws, Administrative Regulations and Department Rules Regarding Intellectual Property Rights (sorted by effective date)
Effective date and amendment date Law, Regulations or Rules Effective date: March 1, 1983 First amendment Date: February 22, 1993 Second amendment Data: October 27, 2001
Trademark Law of the People’s Republic of China
Effective date: April 1, 1985 First amendment Date: September 4, 1992 Second amendment Data: August 25, 2000
Patent Law of the People’s Republic of China
Effective date: June 1, 1991 First amendment Date: October 27, 2001
Copyright Law of the People’s Republic of China
Effective date: May 8, 1997 First amendment Date: November 29, 2001
Rules for Pesticide Administration
Effective date: June 16, 1999 Implementation Rules for the Regulations Regarding the Protection of New Varieties of Plants (Agriculture Part)
Effective date: August 10, 1999 Implementation Rules for the Regulations Regarding the Protection of New Varieties of Plants (Forestry Part)
Effective date: July 1, 2001 First amendment Date: December 28, 2002
Implementing Regulations on Patent Law
Effective date: October 1, 2001 Regulations on the Protection of Layout-Design of Integrated Circuits
Effective date: October 1, 2001 Implementation Rules for the Regulations on Integrated Circuits Design Protection
Effective date: January 1, 2002 Regulations on Computer Software Protection Effective date: February 1, 2002 Management Regulations of Audio and Video Products Effective date: April 1, 2002 Regulations on Protection of the Olympic Symbols Effective date: April 10, 2002 Management Measures of Wholesale, Retail, and Rent of
Audiovisual Production Effective date: June 1, 2002 Management Measures of Audiovisual Production Import Effective date: September 15, 2002 Implementing Regulations on the Copyright Law Effective date: September 15, 2002 Implementing Regulations on Trademark Law Effective date: September 15, 2002 Regulations for the Implementation of Drug Administration
Law Effective date: June 1, 2003 Provisions for Identification and Protection of Well-known
Trademarks Effective date: June 1, 2003 Procedure for the Registration and Administration of
Collective Marks and Certification Marks Effective date: July 15, 2003 Measures on Compulsory Licensing of Patents Effective date: July 15, 2003 Measures for Enforcement of Copyright Administration
Penalty Effective date: September 1, 2003 Measures of the Implementation of Regulations Governing
Customs Protection of Intellectual Property Right Effective date: March 1, 2004 Regulations on the Customs Protection of Intellectual
Property Effective date: November 1, 2004 Regulations on Administration of Veterinary Drug Effective date: December 22, 2004 Interpretations by the Supreme People’s Court and the
Supreme People’s Procuratorate on Several Issue of Concrete Application of Laws in Handling Criminal Cases of Infringing Intellectual Property
Effective date: March 1, 2005 Regulations on the Copyright Collective Administration Source: State Intellectual Property Office of China (SIPO)
51
Appendix 2: List of International Conventions on Intellectual Property Rights China has Acceded to
Date of Accession Name of Treaty Since June 3, 1980, China has been a member state of World Intellectual Property Organization
Convention Establishing the World Intellectual Property Organization
Since March 19,1985, a member state of Paris Convention Paris Convention for the Protection of Industrial Property Since 1989, one of the first member states Treaty on Intellectual Property in Respects of Integrated
Circuits Since October 4, 1989, a member state of Madrid Agreement
Madrid Agreement Concerning the International Registration of Marks
Since October 15, 1992, a member state of Bern Convention
Bern Convention for the Protection of Literary and Artistic Works
Since October 30, 1992, a member state of Universal Copyright Convention
Universal Copyright Convention
Since April 30, 1993, a member state of the Convention Convention for the Protection of Producers of Phonograms against Unauthorized Duplication of their Phonograms
Since January 1, 1994, a member state of the Convention Patent Cooperation Treaty Since August 9, 1994, a member state of the Nice Agreement
Nice Agreement Concerning the International Classification of Goods and Service for the Purposes of the Registration of Marks
Since July 1, 1995, a member state of the Budapest Treaty Budapest Treaty on the International Recognition of the Deposit of Microorganisms for the Purposes of Patent Procedure
Since September 19, 1996, a member state of the Locarno Agreement
Locarno Agreement Establishing an International Classification for Industrial Design
Since June 19, 1997, a member state of the Strasbourg Agreement
Strasbourg Agreement Concerning the International Patent Classification
Since April 23, 1999, a member state of UPOV International Convention for the Protection of New Varieties of Plants
Since December 11, 2001, a member state of the Agreement
Agreement of World Trade Organization on Trade-related Aspects of Intellectual Property Rights
Source: State Intellectual Property Office of China (SIPO)
52
Appendix 3: Definition of the Variables
Variable Description Source IPP1 Denotes the density of intellectual property law firms at the province level, measured as
the number of intellectual property agent companies divided by population in ten thousands. Data of this variable is available from 2002 to 2005. Thus, the variable values in 2001 are also set equal to the value of 2002.
SIPO NBS
IPP2 Denotes technological market size, measured as the transaction volume of technological market in a province divided by provincial GDP.
MOST NBS
Access to New Debt Dummy variable equals 1 if there is a net increase of debt for firm i in year t which exceeds 5% of its total assets at the end of year t.
MOST
Access to New External Equity
Dummy variable equals 1 if there is a net increase of external equity for firm i in year t which exceeds 5% of its total assets at the end of year t, where net increase of external equity is defined as the change in book equity minus the change in retained earnings.
MOST
Informal Financing Ratio
Denotes the net increase of informal financing (debt minus bank loan) for firm i in year t as a percentage of new debt of year t. we only construct this variable for the observations whose access to new debt dummy variables equal 1 in 2004 and 2005, because the data of bank loan is only available in these two years.
MOST
R&D Intensity The research and development expenditure of firm i in year t divided by start-of-year of book asset.
MOST
New Product Sales Ratio New product sales divided by total sales. 82 observations are coded as missing record, because they have zero total sales at that observing point.
MOST
GDP per capita Provincial GDP divided by the population of that province. MOST Banking Provincial banking system development index is the arithmetic average of the standardized
value of following two sub-indexes: first, the competition of financial industry measured as the percentage of deposits taken by non-state financial institutions for each province; second, the transition to open markets in loan allocation measured as the percentage of short-term loans to the non-state sector for each province. Standardized value is calculated according to following formula: Score = (Vi-Vmin)/(Vmax-Vmin)×10 , where Vi is the original score of index i in the period of 2001 to 2005; Vmax and Vmin are the maximum and minimum of the original score of all provinces in base year (2001).
Fan& Wang (2006)
Law This index is a measure of the efficiency of law enforcement in every province, which is obtained from the annual survey on a representative sample of enterprises about the legal environment and judicial efficient in protecting the lawful business activities of the enterprises. Standardized formula is the same as Credit.
Fan& Wang (2006)
Corruption Control Provincial Corruption Control index is the arithmetic average of the standardized value of following two sub-indexes: first, the intervening of the government in business, measured as the time spent by entrepreneurs in dealing with bureaucracy; second, the level of non-tax levies on enterprises (including illegal fees, apportion and fine from local government) as a percentage of sales. Following formula is used to make above two sub-indexed positively correlated with the provincial incorrupt level: Score = (Vmax-Vi)/(Vmax-
Vmin)×10 , where the definition of Vi, Vmax and Vmin is the same as above Banking Index.
Fan& Wang (2006)
Patent Dummy Dummy variable equals 1 if the companies hold any patents before current year. Patent could be invention patent, utility model patent or design patent. According to the companies’ name, we hand collect the data of this variable from SIPO patent search website.
SIPO
Sales Growth Total sales growth rate. Set the value of this variable as the maximum of total sample, if the total sales of previous year equal zero.
MOST
53
Intangible to Total Assets Ratio
Intangible asset divided by total assets. MOST
ROA Net profit divided by total assets. MOST Leverage Book debt divided by total assets. MOST New Debt/Asset Ratio of net increase of debt in a given year to total asset at the beginning of the year. MOST New External Equity /Asset
Ratio of net increase of external equity in a given year to total asset at the beginning of the year. Net increase of external equity is defined as the change in book equity minus the change in retained earnings.
MOST
New Internal Finance /Assets
Denotes the ratio of net increase of retain earnings in a given year to total assets at the beginning of the year.
MOST
Innovation patents Number of Innovation patents created by the company in a given year. We hand collect the data of this variable from SIPO patent search website.
SIPO
Utility model patents Number of Utility Model patents created by the company in a given year. We hand collect the data of this variable from SIPO patent search website.
SIPO
Design patents Number of Design patents created by the company in a given year. We hand collect the data of this variable from SIPO patent search website.
SIPO
Total patents Total number of three types of patents created by the company in a given year. We hand collect the data of this variable from SIPO patent search website.
SIPO
Log(R&D stock) R&D stock for firm i at the end of year t is obtained from the formula: ki,t= (1-δ) ki,t-1 +ri,t, where ri,t is the end-of-period stock of R&D capital and ki,t is the R&D expenditure during the year t. The depreciation rate δ is chosen to be 15%, Because our sample period is only five years, we choose to include only two lagged R&D expenditures.
MOST
Log(Assets) Natural logarithm of total assets MOST Log(Firm Age) Natural logarithm of firm age MOST SOE Dummy variable equals 1 if the companies are registered as state-owned enterprises, or
registered as share-holding corporations while relatively controlled by government institutions.
MOST
POE Dummy variable equals 1 if the companies are registered as domestic Chinese privately-owned enterprises, or registered as share-holding corporations while relatively controlled by private persons or organizations in domestic China.
MOST
Foreign Dummy variable equals 1 if the companies are registered as foreign solely-owned or joint-venture enterprises, or registered as share-holding corporations while relatively controlled by foreign persons or organizations.
MOST
Ethnic Dummy variable equals 1 if the companies are registered as ethnic Chinese solely-owned or joint-venture enterprises, or registered as share-holding corporations while relatively controlled by private persons or organizations from Taiwan, Hong Kong and Macao.
MOST
COE Dummy variable equals 1 if the companies are registered as collective-owned enterprises, or registered as share-holding corporations while relatively controlled by the communities in cities and rural areas. This ownership type is left by the planned economy period. And relatively few companies take this ownership type now.
MOST
Others Dummy variable equals 1 if the companies are registered as share-holding corporations and relatively controlled by legal entities, whose ultimate controller’s ownership-type is not disclosed.
MOST
Foreign Joint Venture Dummy variable equals 1 if the foreign-owned enterprises take the organizational form as foreign-Chinese joint venture instead of foreign solely-owned enterprise.
MOST
Ethnic Joint Venture Dummy variable equals 1 if the ethnic Chinese owned enterprises take the organizational form as ethnic Chinese and local Chinese joint venture instead of ethnic Chinese solely-
MOST
54
owned enterprise. Industry Dummies 21 industry dummies have been included in all equations reported in Table 2 to 7. The
classification of industry refers to “Industry Classification Standard of Chinese Listed Companies”. Each dummy takes the value 1 if the firm main activity is in that industry, and zero otherwise.
MOST
Regional Dummies Four geographic regional dummies have been included in equations reported in Tables from 2 to 7. Referring to the regional division from Development Research Center of China State Council, we using a partition of the territory into five regions: Northeast (Heilongjiang, Jilin, Liaoning), Coastal (Anhui, Beijing, Fujian, Guangdong, Hainan, Hebei, Jiangsu, Shandong, Shanghai, Tianjin, Zhejiang), Central (Henan, Hubei, Hunan, Jiangxi, Shanxi), Northwest (Gansu, Neimenggu, Shaanxi, Xinjiang), Southwest (Chongqing, Guangxi, Guizhou, Sichuan, Yunnan).
MOST