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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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