Does Enforcement of Intellectual Property Rights Matter in China ...

Does Enforcement of Intellectual Property Rights Matter in China? 

Evidence from Financing and Investment Choices in the High Tech Industry* 

James Ang, Yingmei Cheng, and Chaopeng Wu 

James S. Ang 

Department of Finance 

Florida State University 

Tallahassee, FL 32306-1110 

Telephone: (850) 644-8208 

Fax: (850) 644-4225 

E-Mail: jang@cob.fsu.edu 

Yingmei Cheng 

Department of Finance 

Florida State University 

Tallahassee, FL 32306 

Telephone: (850) 644-7869 

E-mail: ycheng@cob.fsu.edu 

Chaopeng Wu 

School of Management 

Xiamen University 

Xiamen 361005, P. R. China 

E-Mail: wuchaopeng@163.com 

First draft: October 28, 2008 

Revised: January 15, 2011 

* We appreciate comments and suggestions from participants at Western Finance Association Meeting 

(June 2009), Annual Darden International Finance Conference (University of Virginia, March 2009; 

National University of Singapore, May 2009), FMA Asian Conference (May 2009), FMA European 

Conference (June 2009), China International Finance Conference (July 2009), Xiamen University 

(China, May 2009), Florida State University School of Law (May 2009), and ASSA Annual Meeting 

in Denver (January 2011). Wu gratefully acknowledges financial support from the National Natural 

Science Foundation of China (grant 70632001).


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 and the 

problem of information asymmetry. We show that the effective enforcement at the provincial level is 

critical in encouraging financing and investing in R&D, using a unique and rich database of high 

technology firms. These firms are located in twenty-eight provinces/districts throughout China. To 

identify the causal effect of provincial-level IP rights enforcement on firm behavior, we use 

instrumental variable regressions to deal with the problem of endogeneity. We find that the 

enforcement of IP rights positively affects firms’ ability to acquire new external debt (including formal 

and informal financing). Firms in provinces with better enforcement of IP rights invest more in R&D, 

generate more innovation patents, and produce more sales from new products. The results confirm that 

enforcement of IP rights matters even in China. The evidence also suggests that facilitating financing 

and investing in R&D are the channels through which better IP rights enforcement could affect the 

growth of the economy. 

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Evidence from Financing and Investment Choices in the High Tech Industry 

1. Introduction 

Allen, Qian and Qian (2005) 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 expenditure in China in spite 

of its generally perceived weak protection of intellectual property (IP) rights. The growth of China’s 

R&D expenditure ranked first among 40 OECD (Organization for Economic Co-operation and 

Development) countries and selected non-member economies from 2002 to 2010, while its IP 

protection is still considered very weak in comparison to other countries (Israel, 2006; Stratford, 

2006; International Intellectual Property Association, 2007). 1 The China phenomenon has become 

such an anomaly, as some cross-country studies are unable to obtain the expected positive relation 

between IP 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 IP protection 

does not matter there? That is the question we address in this paper. 

Effective protection of intellectual property rights depends both on the existence of IP laws and 

the enforcement of the laws. 2 

Although much has been written about the IP rules and laws (e.g., 

Gould and Gruben, 1996; Moser, 2005), there is little empirical evidence on the importance of their 

enforcement. One reason is that studies of IP protection are generally performed at the country 

1 We obtain the information from the OECD website, http://stats.oecd.org/Index.aspx and R&D Magazine: 

http://www.rdmag.com/. 

2 La 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. 

3

level. 3 

Country-level analysis does not allow researchers to separate the confounding effect of the 

existence of IP laws and the effectiveness of their enforcement. Rather than dealing with crosscountry 

variations in IP laws, we focus on a single country, China. We analyze the impact of local 

enforcement of IP laws on the financing of and investing in R&D by firms in provinces throughout 

China. 4 

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 IP laws and 

international treaties are the same for all provinces in China, there exist significant differences in the 

local enforcement of the IP laws. Our approach of analyzing provincial variations parallels that of 

Guiso, Sapienza and Zingales (2004) who study the difference in regional social capital in Italy, and 

Benfratello, Schiantarelli, and Sembenelli (2008) who investigate the effect of local banking 

development on firms’ innovative activities in Italy. 

We propose that better enforcement of IP rights mitigates the problems associated with R&D: 

risks of appropriation by competitors and information asymmetry. This leads to the empirically 

testable hypotheses that better enforcement of IP rights helps generate more funds available to 

finance R&D, more investment in R&D, and more output from R&D. 

3 Comparisons 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 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. 

4 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). 

4

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) provides firm-level financials and R&D related information for a large number of 

Chinese high tech companies. These firms are unlisted companies. We choose to focus on these 

firms for the following reasons. First, the success of high tech firms critically depends on the output 

from R&D and the legal protection of their exclusive rights. Second, listed firms in China are not 

required to disclose detailed information on their R&D activity, while our database of unlisted firms 

provides richer details, from R&D expenditure to R&D personnel composition. Third, since unlisted 

firms are unable to raise funds from the stock market, the financing of their R&D activity is of 

particular interests. 

The enforcement of intellectual property rights is many faceted. We introduce two measures of 

IP rights enforcement. The first is the fraction of intellectual property infringement cases won by the 

plaintiffs in each province, thereafter labeled as “IPP1”. It directly measures the probability of 

winning by the plaintiffs in IP infringement cases, and thus a good proxy for the enforcement of IP 

rights in the courts. The second is the number of articles that advocate IP protection in the official 

newspaper published by provincial Communist Party, thereafter labeled as “IPP2”. This variable 

conveys the IP enforcement policy from top provincial officials to all levels of local administrative, 

judicial, and law enforcement agencies, since these local officials are invariably Party members. 

The first measure of IP rights enforcement examines how well a province enforces laws and 

regulations on intellectual properties via its court system. The court is an integral part of the IP 

rights enforcement, where it can order the defendant to cease infringement, compensate the plaintiff, 

or destroy the confiscated products. We collect the outcomes of IP infringement cases brought 

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efore the courts with the IP owners as the plaintiffs. We compute IPP1 as the percentage of these 

cases ruled in favor of the plaintiffs. We expect that a better enforcement of intellectual property 

rights is manifested in the greater odds that the intellectual property owners will win their cases in 

courts. 

Chinese authorities often announce policy directives through the Communist Party’s official 

newspaper. The second measure of IP rights enforcement is the number of articles advocating IP 

protection published in the official newspaper of the provincial Communist Party, which represents 

the highest level of the provincial administration. Articles published in the official newspaper serve 

to convey policy guidelines to all levels of local governments. A greater number of articles related to 

IP protection transmit a stronger commitment to IP enforcement by the top provincial officials. 

While IPP1 reflects the enforcement effectiveness of courts, IPP2 monitors the attitude of the top 

provincial officials towards IP enforcement. 

One concern about the causal effect of IP rights enforcement on financing of and investing in 

R&D is that some unobserved provincial factors may contribute to both IP rights enforcement and 

financing of and investing in R&D, thus producing a potentially spurious relationship between IP 

enforcement and financing of and investing in R&D in the OLS regressions. The omitted variable 

bias is a common source of the endogeneity problem. Another source of the endogeneity problem is 

due to reverse causality: a greater level of financing and investment may increase the demand of IP 

protection. We utilize instrumental variable regressions to deal with the problem of endogeneity. To 

choose the right instruments, we go back to the pre-modern history of China and identify the 

influence of western ideology on the enforcement of IP rights. The two instruments are: 1) Christian 

Colleges, i.e., the number of colleges founded by Christian missionaries in the provinces by 1920, 

6

and 2) British Settlement, a dummy variable that is one when a province had a concession or leased 

territory established by Great Britain during the late Qing Dynasty. 5 They are expected to be 

positively and significantly related to the two measures of IP rights enforcement, yet they are not 

likely to directly affect the financing and investment of firms in the 21 st century. The results from 

the partial F-tests verify that the two instruments are indeed strong instruments, and the 

overidentification tests suggest that they satisfy the exclusion restriction. 

We show high tech firms in provinces with better enforcement of IP rights enjoy greater access 

to external debt, invest more funding in R&D, and generate more patents and greater new product 

sales. Our results demonstrate that protection of IP rights matters even in China. Better IP rights 

enforcement helps to facilitate financing of and investing in R&D, and therefore stimulate 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, measures of provincial-level IP rights 

enforcement, and other control variables. Section 4 reports the empirical results of the effect of IP 

rights enforcement on external financing, R&D input, and R&D output. Section 5 concludes. 

2. Intellectual Property Rights Enforcement and R&D Investment 

In this section, we describe IP rights enforcement in China, and discuss the risks of 

appropriation by competitors and the problem of information asymmetry. These issues could lead to 

under-provision of funding and under-investment in R&D. Finally, we develop hypotheses of how 

better IP rights enforcement may mitigate these problems. 

5 The concession or leased territory of British was in Xiamen city in Fujian, Hankou city in Hubei, Jiujiang 

city in Jiangxi, Zhenjiang city in Jiangsu, Guangzhou city in Guangdong, Weihai in Shandong, Tianjin, and 

Shanghai. 

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2.1 IP rights enforcement in China 

China is often criticized as having a poor record in the protection of IP rights (see Wang, 2004; 

Maskus, Dougherty and Mertha, 2005). However, an examination of China’s IP laws (Appendix 1) 

and the international treaties it has acceded to (Appendix 2) put China at par with the more 

developed economies. 6 Two factors may explain the discrepancy between written laws and common 

perception. 

The first is that most of the IP laws in China came into existence in the relatively recent period. 

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 2008: the licensing fees grew 

substantially, increasing from $13.86 billion in 2004 to $27.13 billion in 2008. The large numbers in 

Figure 1 suggests that there exists significant IP rights protection in China at least in the recent 

period, contradicting common perceptions. 

Insert Figure 1 

The second is that the effective protection of IP rights depends on both the existence of IP laws 

and the enforcement of the laws. Even though the applicable IP laws and international treaties are 

the same within China, there exist significant differences in the local enforcement of the IP laws, as 

we find in our study. One has to consider local differences in enforcement to assess effectiveness in 

protecting IP rights. 

6 Of 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). 

8

These two factors suggest that it is worthwhile to investigate local differences in the IP 

enforcement in the more recent period (2001 and later). Our focus is to study how provincial-level 

enforcement of IP rights helps to resolve the problems associated with R&D. 

2.2 Risks of appropriation by competitors and information asymmetry 

The competitive capabilities of high tech enterprises are largely determined by their intellectual 

properties, 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 properties. 

Innovative ideas are often imitated and appropriated by 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 could be 

reluctant to finance and invest in R&D, which in turn reduces a country’s economic growth. 

Information asymmetry could also reduce the amount of financing of and investment in R&D. 

In the context of our analysis, the problem of information asymmetry refers to the fact that firms are 

unwilling to disclose confidential information to potential lenders/investors, because these fund 

providers may appropriate the knowledge (Anton and Yao, 2002). Ueda (2004) analyzes a situation 

in which a venture capitalist could pose a threat by stealing ideas and projects from the 

entrepreneurs, and suggests that stronger protection of IP rights could mitigate the problem and 

encourage entrepreneurs to seek financing from venture capitalists. 

2.3 The effect of IP rights enforcement on external financing 

For those who infringe on others’ intellectual properties, they have a higher probability of 

facing legal consequences in provinces with better enforcement of IP rights. More effective 

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enforcement raises the costs of imitation and infringement. Consistent with the model in Ueda 

(2004), better protection of IP rights will also punish lenders/investors who steal ideas of 

innovations, 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 the problem of information asymmetry, high tech firms should be able to 

receive more outside debt financing in provinces with better IP rights enforcement (Hypothesis 1). 

Because these are unlisted companies, new equity could only come from existing shareholders. In 

the pecking order of financing preferences, asking current shareholders for fresh capital infusion is 

ranked below debt. Thus, we expect that having greater ability to acquire external debt under better 

IP rights enforcement should alleviate the need to obtain new equity contributions from existing 

shareholders. We propose that better IP rights enforcement reduces the amount of new external 

equity raised (Corollary 1). 

2.4 The effect of IP rights enforcement on informal financing 

China’s formal financial system is large but still underdeveloped; it is mainly controlled by the 

four largest state-owned banks. 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 credits. As shown in Allen, Qian and Qian 

(2005), Chinese firms in private sectors raise only about 10% of total financing from banks, 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 loans are still not the main source of 

financing. Thus, we have a financing aspect of the China puzzle: how could firms finance growth 

10

when the roles of formal financing channels are relatively small and narrow (very few large banks) 

or virtually nonexistent (a lack of corporate bond market)? 

Filling the financing gap are trade credit and various informal sources of financing. Some 

examples of informal financing include funding from friends, family, and community, rotating 

savings groups, underground financial institutions, and inter-corporate lending. On one hand, these 

informal sources lack legally enforceable means to possess borrowers’ assets as security, thus their 

payoff depends critically on the borrowers’ ability to generate cash flows. Poor enforcement of IP 

rights can increase the probability and magnitude of appropriation by the borrowers’ 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 including other competitors they have financed (similar to the venture capital situation 

modeled by Ueda, 2004). 

Firms in provinces with poor IP rights enforcement are therefore 

discouraged to seek financing from informal lenders. These considerations, taken together, suggest 

that there is a positive relationship between IP rights enforcement and firms’ 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 

hypothesize that high tech firms in the provinces with better IP rights enforcement invest more funds 

in R&D (Hypothesis 3). Firms in provinces with better IP rights enforcement receive greater 

protection from patent infringement, therefore they are more likely to seek patent generation, 

registration, and application. Thus we expect better IP rights enforcement increases the number of 

patents generated by firms (Hypothesis 4A). Poor IP rights enforcement makes stealing of 

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intellectual properties possible through patent infringement, imitation, etc., which reduces new 

product sales. Therefore, we expect better IP rights enforcement increases firms’ sales from new 

products (Hypothesis 4B). 

3. Measures of IP Rights Enforcement, Provincial Characteristics, and Database Description 

In this section, we describe our two measures of provincial-level IP rights enforcement, 

provincial characteristics, and other controlling factors. In addition, we specify the data sources and 

present a preliminary description of the high tech firms in our sample. 

3.1 Measures of provincial-level IP rights enforcement 

The provinces in China are under the same sets of national laws and regulations; however, local 

enforcement can vary a great deal. We measure provincial enforcement of IP rights in two ways. 

The first is the fraction of the IP infringement cases won by the plaintiffs (the IP owners) in the 

provincial courts. It directly measures the probability of winning by the IP owners in IP infringement 

cases, and thus is a good proxy for the enforcement of IP rights. We label it as “IPP1”. The second 

is the local authorities’ willingness to enforce IP rules. It is measured by the number of articles that 

advocate IP protection each year in the official newspaper published by the provincial Communist 

Party. This variable gauges the attitude towards IP enforcement by provincial officials. We label it 

as “IPP2”. 

To construct IPP1, we download and read the written judgment of 7,668 IP infringement cases 

filed in 28 provinces in 2001-2005 from the China Judicial Case Database, with the IP owners as the 

plaintiffs. The cases include patent right infringements, infringements of other companies’ 

technologies, commercial secret infringements, new plant infringements, and integrated circuit 

design infringements. We only consider high tech IP infringement, excluding trademark and 

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copyright infringement cases. We believe that the court judgments in high tech IP infringement 

cases are the most relevant to our study, since all our sample firms are high tech companies. We 

identify that a case is won by the plaintiff if the court orders the defendant to cease infringement, 

compensate the plaintiff for its loss due to the infringement, destroy the infringing products and 

equipments involved, and/or pay the legal cost of the lawsuit. The case is won by the defendant if 

the court dismisses the lawsuit of the plaintiff. 

Two possible scenarios may create bias in using the fraction of cases won by the plaintiffs as 

the measure of IP enforcement. One concern is that plaintiffs may initiate lawsuits with no merit. 

Given that there are substantial monetary costs in going to Chinese courts, it is highly unlikely that 

plaintiffs will initiate nuisance lawsuits with no merit and thus face no or low chance of winning. 7 

Another deterrent for a firm to bring up a nuisance lawsuit is the adverse impact on its reputation, or 

“loss of face”. Given the large number of lawsuits, even if there were a handful of nuisance ones, it 

is reasonable to assume that they will not affect the measure in any meaningful way. In addition, 

there is no evidence to lead us to believe that the incidence of no-merit lawsuits should be 

systematically different across the provinces. The other argument is that better IP enforcement may 

lead to fewer lawsuits, which is not captured by the fraction of lawsuits won by plaintiff. However, 

the expectation of better enforcement in the courts means higher expected returns for the intellectual 

property owners to pursue the violators in courts. Indeed we find that provinces with a higher 

winning percentage for the IP owners actually have a greater number of IP lawsuits. 

Provincial Communist Party is a province’s highest administration. Its official newspaper is 

7 Legal expenses for law suits between businesses are quite substantial in China. Losers of lawsuits pay 3%- 

10% of the disputed amount for counsel fees, plus 0.2%-2.5% for brief fees. Winners pay even more: 10-20% 

of the disputed amount for counsel fees. 

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circulated daily, with the main purpose of promoting provincial policies and affecting public 

opinions. All provincial government bodies at all levels must subscribe to this newspaper. Its 

readers include persons in all fields, in addition to government personnel. Being the only political 

Party in China, the attitude of the provincial Communist Party towards IP enforcement is critical in 

the protection of IP rights. A stronger advocacy of IP rights by the Party’s official newspaper in a 

province is expected to lead to better IP enforcement. We search for relevant articles in the 

newspaper using the following keywords: IP rights protection, patent right protection, trademark 

protection, copyright protection, fighting against IP infringement, fighting against trademark 

infringement, and fighting against copyright infringement. Then we read through the articles to 

make sure they indeed advocate IP rights protection. We use the full-text database of “China’s 

major newspapers” to search for the articles. A few Party newspapers are not included in the 

database in some of the years, which results in 90.9% coverage of all provincial Party newspapers. 8 

Insert Table 1 

Table 1 (Panel A) shows that the mean of IPP1 is 72.4%. What is most relevant for this study is 

its large variation across provinces, ranging from a low of 25% of IP cases won by the plaintiffs in 

the courts to a high of 87.5%. The map in Figure 2 demonstrates that this measure of IP rights 

enforcement varies greatly within China. Enforcement is best in the coastal region of China, weaker 

in Central China, and weakest in the Northeast and Southwest regions. However, even within each 

8 One question about IPP2 is that: is it just cheap talk? We have observed that the “talk” does translate to 

action. A number of cases reported by the press, including Wall Street Journal, link the Chinese authorities’ 

promise to enforce IP rules with real actions to protect intellectual properties. One such example involves 

Zhejiang province. Zhejiang is one of the provinces with the highest IPP2 in our sample, i.e., it has the highest 

number of articles calling for IP protection. The capital of Zhejiang Province, Hangzhou, has “set targets for 

enforcement of intellectual-property rights”, and attracted a series of investment from Microsoft, including 

two new technology centers and other investments (Wall Street Journal, May, 16 th , 2009). 

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oad region, there are substantial variations across provinces. IPP2 has a mean of 8.535 with the 

standard deviation of 6.464. It ranges from 0 (not a single mention of IP protection by the official 

Party newspaper in Hainan in 2001) to 19 (19 times calling for IP protection by the official Party 

newspaper in Beijing in 2003). 

Insert Figure 2 

3.2 Database of high tech firms 

The Ministry of Science and Technology (MOST) conducted an annual survey of unlisted high 

tech companies from 2001 to 2005. The surveyed companies are approved as high tech enterprises 

by the local Science and Technology Bureau, and are permitted to locate in the National Innovative 

and High Technology Industrial Development zones in designated cities throughout China. 9 

The 

numbers of companies in the surveys are: 8,298 (for 2001), 9,743 (2002), 11,470 (2003), 13,261 

(2004), and 15,459 (2005). Out of the 58,231 firm-year observations, we exclude firms that report 

no R&D expenditure or have no R&D personnel during the sample period, which leaves us with 

40,845 firm-year observations. 10 Our regression models use one-year-lagged variables, thus firms 

that do not have data in at least two consecutive years are excluded. The final sample includes 

23,995 firm-year observations. 

The surveyed samples are expected to cover all of China’s unlisted high tech companies in 

9 In China, as in many countries, high tech firms in the special zones enjoy tax preferences and other policy 

support. 

10 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 that do not seem to be actively engaged in R&D. It may be 

the case 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. For example, Shijiazhuang Zhenxing Clothing Production Co. 

Ltd., has not obtained the high-tech enterprise qualification but is located in Shijiazhuang High-tech Industrial 

Development Zone for preferential treatment. 

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these designated zones because the annual survey is mandatory for all the qualified companies. The 

survey collects information on balance sheet, income statement, details of R&D spending, sources 

of R&D expenditures, R&D personnel composition, and R&D output such as new product sales. 

MOST collected the information to monitor the operation and development of high tech companies 

and to provide potential inputs to future policy making. This database has not been analyzed in 

academic studies. 

In comparison to those used by 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 

publicly listed companies, and generally cannot be found for unlisted companies. Second, compared 

to the other Chinese database, the NBS (National Bureau of Statistics) which only tracks 

manufacturing firms, our database covers high tech companies in various industries. The top five 

industries in our sample are Computer & Communications (30%), Machinery (large and small) 

(30%), Drugs & Medical (8%), Chemical (8%), and Electrical & Electronic (5%). In addition, we 

hand-collect patent data from the State Intellectual Property Office of China (SIPO)’s patent website. 

We focus on the numbers of innovation patents obtained by a firm in a given year. 

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. Consistent with previous studies 

(Hovakimian, Opler and Titman, 2001; De Haan and Hinloopen, 2003), the 5% cut-off point is used 

16

to ensure that the analysis focuses on relatively substantial financing events. More than 47% of firmyear 

observations have raised new debt. 

Second, we analyze bank loan and informal financing separately. Since information on detailed 

debt structure is limited, we compute the informal financing ratio as the net increase of all debt in a 

given year minus the net change in bank loan and net change in trade credit, divided by the net 

increase of all debt. MOST dataset does not disclose the trade credit of individual firms, so we use 

instead, the median ratio of new trade credit to the net increase of all debt for the listed companies in 

the same industry-year, in the following calculation: 

Debt - Bank Loan Trade Credit 

Informal Financing Ratio 

-( ) 

Debt 

Debt 

same industry- year 

The classification of industry is from Industry Classification Standard by CSRC (China Securities 

Regulatory Commission). If net new total debt is less than the sum of new bank loan and new trade 

credit in a year, the informal financing ratio is negative, i.e., the firm repays previous borrowing 

from informal sources with new bank loans and new trade credit. If the sum of new bank loan and 

new trade credit is negative and net new total debt is positive, informal financing is the source of 

funds to pay back previous bank loan and trade credit. In this case, the informal financing ratio is 

larger than 1. Because the information on bank loan is available only in 2004 and 2005, this portion 

of the analysis is limited to these two years. We only calculate the informal financing ratio for the 

firms that raised new debt exceeding 5% of their total assets. We have 6,487 firm-year observations 

with informal financing ratios. 

As shown in Table 1 (Panel A), 28.5% of new debt comes from informal financing. The 

calculation only includes firms that raised new debt exceeding 5% of their total assets. Informal 

financing ratio in the total sample is about 12%. This number is higher than the average percentage 

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of informal financing ratio (8%) for the sample of Chinese firms in Ayyagari, Demirguc-Kunt, and 

Maksimovic (2010). The reason is that our sample firms are younger and have a higher sales growth 

than theirs. Annual sales growth of our samples are 134% (mean) and 18.3% (median) during 2002- 

2005, while theirs are 13% (mean) during 1999-2001. Firms in our sample, on average, have been 

in business for 7.8 years, while theirs are 16 years old. The higher informal financing ratio in the 

younger firms is consistent with the suggestion by Allen et al. (2005) that “Financing for private 

credit agencies (informal finances), instead of banks, is the most important channel during a firm’s 

growth period (year 3-8)” (Page 95). 

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 18.4% of firm-year observations have 

recorded new external equity. 11 

Table 1 (Panel A) also reports the summary statistics for R&D input and R&D output. We 

measure R&D input as R&D intensity, defined as R&D expenditures of firm i in year t divided by 

the start-of-year book assets. R&D intensity averages at about 7.6% of assets and exhibits wide 

variation, with a standard deviation of 13.0%. R&D output for these high tech firms is measured as 

the number of innovation patents and the percentage of new product sales to total sales. The number 

of innovation patents has mean value of 0.81, with a standard deviation of 22.64. And the mean 

value of percentage of new product sales is 21.3%, with a standard deviation of 34.4%. 12 

11 Since our sample only includes unlisted companies, the companies mainly raise new equities from existing 

shareholders. 

12 292 observations are coded as missing, because these high tech firms have no reported sales. 

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To deal with outliers, we winsorize the variables at the 1 st and 99 th percentiles, for 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), leverage, new debt to total assets, new 

external equity to total assets, and new internal equity to total assets. Detailed description of these 

firm-level variables can be found in Appendix 3. 

3.4 Provincial characteristics 

To ensure that the estimated impact of the IP rights enforcement on firm financing and 

investment decisions is not due to other sources of provincial differences, we collect and compute 

various provincial-level characteristics. As a measure of economic development, we compute the 

growth in GDP for each province in the previous year. The relevant data is from the National Bureau 

of Statistics. There is a wide variation in the provincial GDP growth rates, from 6.8% to 20.9%, with 

a mean of 12%. 

We also control for the differences in provincial institutions using indices collected by the 

National Economic Research Institution (NERI). 13 The first is the provincial index of banking 

system development (denoted as “Banking Development”) in the previous year, which is based on 

two dimensions: competition in the financial industry, measured as the percentage of deposits taken 

by non-state owned financial institutions, and transition to free economy in loan allocation, 

measured as the percentage of short-term loans to firms in the non-state sectors. A greater value of 

“Banking Development” assigned to a province is associated with a better developed banking 

13 NERI indices are constructed by Fan and Wang (2001, 2004, 2007, and 2010). 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, along 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. 

19

system. The variation of this index is substantial: it ranges from 0.85 to 11.07 with a standard 

deviation of 1.81. The second index is the provincial government’s control of corrupt practices 

(denoted as “Corruption Control” thereafter) in the previous year. It has two components: the extent 

the local government intervenes in businesses, measured as the time spent by businesses in dealing 

with bureaucracy, and the 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. 

“Corruption Control” varies from 1.765 to 12.350, with a standard deviation of 2.07. A higher value 

of “Corruption Control” corresponds to a lower level of corruption. 

In addition, the presence of large urban centers or well-functioning local universities might 

affect the availability of external financing and R&D investments for high tech firms. To capture 

these effects, we use two control variables: first, the dummy “Metropolis” is one if there is at least a 

city in the province with over two million inhabitants; second, the variable “University” is the 

number of universities in the province. 

We link the above provincial characteristics with the corresponding firm-level data, via 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 have National Innovative and High Technology Industrial 

Development zones, so these provinces do not have high tech companies in our sample. Thus, the 

total number of provinces/districts in our analysis is 28. Panel B of Table 1 reports the correlation 

coefficients between IP rights enforcement and provincial-level variables. 

4. Empirical Analysis and Results 

We report our empirical analyses and results in three subsections. We examine the impact of IP 

20

ights enforcement on financing, R&D input, and R&D output. The evidence suggests that 

facilitating financing and investing in R&D are the channels through which better IP rights 

enforcement could affect, in the aggregate, the growth of the economy. 

4.1 The effect of IP rights enforcement on financing 

4.1.1 Access to new debt 

We examine the relationship between provincial-level IP rights enforcement and the individual 

firms’ ability to obtain external debt. We use a probit regression model for our 23,955 firm-year 

observations. To control for other factors that may affect these firms’ ability to obtain external debt, 

we include firm- and provincial-level characteristics. The model is specified as the following: 

Y = α + β IP Rights Enforcement + β Firm Variables + β Provincial Variables 

i,t 1 i,t-1 2 i,t-1 3 i,t-1 

+ β Industry Dummies + ε 

5 i i,t 

(1) 

The dependent variable, 

Y 

i , t 

, is one if the net increase in debt is at least 5% of total assets in year t, 

and zero otherwise. The set of firm-level variables include: patent dummy (which is coded as 1 if 

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 include provincial banking system development and corruption control, provincial GDP 

growth, the number of universities in a province, as well as the metropolis dummy. All independent 

variables in the regression model are lagged by one year. We also add 21 industrial dummy variables 

according to Industry Classification Standard of CSRC. The standard errors are corrected for 

heteroskedasticity and within-province correlations. 


Table 2 (Columns 1 and 2) reports the probit estimates of the marginal effect of IPP1 (the 

21

percentage of IP infringement cases won by the plaintiffs) on the probability that a high tech 

company has access to new debt. Column 1 of Table 2 shows that IP rights enforcement 

significantly increases the probability of a firm having 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 7.9%, holding all other independent variables at their mean values. When other 

provincial characteristics are included in the regression (Column 2), the probability increases by 

6.8%. All the firm-level control variables have the expected signs: having patents, higher R&D 

intensity, larger sales growth, and higher profitability (ROA) increase the probability of a firm 

obtaining new debt, while intangible to total assets ratio, leverage, and firm age have the opposite 

effect. Column 4 and 5 report the results from the probit analysis when IPP2 is used to measure IP 

enforcement. IPP2 has a significant and positive impact on the probability of acquiring new debt by 

firms. 

We are concerned that the observed impact of IP rights enforcement on access to external debt 

might be driven by omitted variables, or the financing may increase the demand of IP rights 

enforcement. To address the endogeneity problem, we employ the instrumental variable (IV) 

approach in a two-stage least square (2SLS) framework. A valid instrumental for IP rights 

enforcement needs to satisfy two conditions: it is correlated with the IP rights enforcement measures 

(the validity requirement), but it needs to be uncorrelated with the residual in the regression of 

access to external debt (the exclusion restriction). 

In choosing suitable instruments for IP rights enforcement, we go back in history to search for 

the origin of the notions of IP protection. One variable that is related to the respect for intellectual 

properties is whether a province had a British concession, which lasted from the late Qing dynasty to 

22

the early years of the Republic. There are two reasons to choose this instrument: first, England is 

generally regarded as the country where copyright law (Stature of Anne, 1710) and patent law 

(Stature of Monopolies, 1623) were originated. Second, the British modeled the local administrative 

and legal systems in the concessions to that in England, and there was evidence of their involvement 

in IP protection in China and enforcement activity by the local Chinese officials, as early as 1902. 14 

We also recognize the importance of the educational institutions in instilling Western values 

among Chinese people. This is especially true for colleges sponsored by Christian churches, since 

Christian value includes the respect for private properties, e.g., the Commandments in Exodus. Thus, 

we choose as our second instrument, the number of colleges in a province founded by Christian 

missionaries (they include American, British, Canadian, Norwegian, and South American, etc.) by 

1920 (Stauffer, Wong and Tewksbury, 1922). Well-known examples include St. John’s University, 

founded by the American Episcopal Church, and Soochow University, founded by the American 

Methodist Missionaries. 

These two instruments help explain local IP rights enforcement, as exposure to Western 

institutions and Christian values may plant the seed of enforcing IP protection in these provinces. 

The instruments are obtained from information predating the modern period, about a hundred years 

14 For example, in 1902, Article 7 of the “Amendment to the Anglo-Sino Trade and Navigation Treaty” stated: 

“England will protect Chinese merchants’ trademark, from imitation by British citizens. Thus, Chinese should 

accord the same protection to British merchants against violation by Chinese citizens.” In 1907, the British 

Counsel in Shanghai demanded the Chinese authority to prohibit Chinese merchants from violating British 

trademarks. In April of the same year, in response to repeated requests by British merchants and counsel, the 

local officials announced, “We find evidence of unscrupulous persons using British trade names for local 

tobacco products. They are instructed to post 1,000 notices admitting responsibility in counterfeiting the 

tobacco product. After this, repeated offenders will be severely punished, there will be no leniency.” (Source: 

The Anglo American Tobacco Company business activity in China, volume 2, China Publishing House, 

Beijing, 1983). 

23

efore our sample period. They should not directly affect the current financing and investment 

decisions of the firms in our sample. 

In Column 3 and 6 of Table 2, we report the estimated results from the instrumental variable 

regressions. Other covariates in the first-stage regression are omitted for presentational convenience. 

In the first stage, the two instruments are significant and positive determinants of IP rights 

enforcement. Larcker and Rusticus (2010) demonstrate that when the instrument is only weakly 

correlated with the endogenous variable, IV methods can produce highly biased estimates even 

when the variable is only slightly endogenous. This is the so-called weak-instrument problem. In 

their survey of the weak-instrument literature, Stock et al. (2002) develop benchmarks for the F- 

statistic; when the number of instruments is 1, 2, 3, 5, and 10, the suggested critical F-values are 

8.96, 11.59, 12.83, 15.09, and 20.88, respectively. If the first-stage partial F-statistic falls below 

these critical values, the instruments are considered to be weak and the validity of inference is 

potentially compromised. The reported partial F-test statistics in the first stage confirm that 

“Christian Colleges” and “British Settlement” are indeed strong instruments, with an F-statistic of 

513.49 (in Column 3) and 2,636.71 (in Column 6). 

In the second-stage, we find that the coefficients of IPP1 and IPP2 are larger than that in the 

probit model. Although we cannot directly test whether a single instrumental variable is uncorrelated 

with the error term in the regression of access to external debt, we can conduct the overidentification 

test of whether a set of instrumental variables (IVs) satisfy the exclusion restriction, when the 

number of instruments is greater than the number of endogenous regressors (Wooldridge, 2002). The 

reported J-statistics from the overidentification test cannot reject the null hypothesis that the two 

instruments are not correlated with the error term in Equation (1), thus the two instruments satisfy 

24

the exclusion restriction criterion. 

In Table 3, we examine the impact of IP rights enforcement on new external equity. In the 

probit regressions, IPP2 has a negative and significant impact on the new external equity as 

predicted, while the coefficient of IPP1 is insignificant or weak. In the IV regressions, however, 

neither measure of IP rights enforcement is significant. Therefore, the support for Corollary 1 is 

weak, at best. 


4.1.2 Informal financing ratio 

We now 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. The difference is that we now use linear regressions, since the dependent 

variable is a continuous variable. The standard errors are robust to correction for heteroskedasticity 

and within-province correlations. 


Column 1 of Table 4 shows that IPP1 increases informal financing ratio, as predicted in 

Hypothesis 2. A one-standard-deviation increase in IPP1 raises the informal financing ratio by 2.6 

percentage points. Moving from lowest-IPP1 province to the highest-IPP1 province increases the 

informal financing ratio by 13.8 percentage points, about half of the sample mean. 

After controlling for the provincial-level characteristics, IPP1 still has a positive and 

statistically significant effect on the proportion of informal financing in new debt (Column 2). The 

development of banking system has a negative and significant effect on the informal financing ratio, 

i.e., better banking development enables firms to rely less on the more expensive external finance. 

25

Greater corruption (less corruption control) is associated with lower informal financing ratio. The 

reason is that greater corruption is found to be associated with more funding from banks (Fan, Rui, 

and Zhao, 2008) and thus, by the substitution effect, a lower ratio of informal financing relative to 

bank loans. GDP growth and provincial number of universities have positive and significant 

coefficients. The regressions using IPP2 as the measure of IP enforcement generate similar results 

(Column 4 and Column 5). 

In the instrumental variable regressions, the impact of IPP1 (IPP2) is greater (smaller) than 

those reported in OLS regressions. The two instruments are strong determinants of IP enforcement, 

and the overidentification tests further confirm they are valid instruments. 

4.2 The effect of IP rights enforcement on R&D input 

We estimate the effect of IP rights enforcement on firm-level R&D investment. 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 to R&D. The OLS regression 

takes the following form: 

R&D Intensity = α + β Sources of Funds + β (Sources of Funds IP Right Enforcement ) 

i,t 1 i,t 2 i,t i,t-1 

3 i,t -1 4 i,t -1 5 i 

ε i,t 

+ β IP Rights Enforcement + β Provincial Variables + β Industry Dummies + 

(2) 

We define the dependent variable, R&D Intensity, as R&D expenditures 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 IP rights enforcement. 15 The funding sources are 

scaled by total assets (the detailed description is given in Appendix 3). Also included are provincial 

characteristics, and industry dummies. All independent variables, except for the new funding 

15 Internal financing is profit minus dividend, adjusted for withdrawal of equity. 

26

variables, are lagged by one year. 

The standard errors are robust to corrections for 

heteroskedasticity and within-province correlations. 


The coefficients and z-stat obtained from the OLS estimation of Equation (2), using IPP1 as the 

measure of IP enforcement, are presented in Column 1 in Table 5. The coefficients on the 

interaction terms between IP rights enforcement and funding from new debt, and between IP rights 

enforcement and internal financing are positive and significant. 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 financing in R&D. When moving from the lowest-IPP1 

province to highest-IPP1 province, the percentage of new debt invested in R&D more than doubles, 

from 3.1% to 8.5%, while the percentage of new internal financing invested in R&D rises from 

9.2% to 20.6%. 

The results above are robust to controlling for other provincial characteristics (Column 2). We 

also re-estimate the basic model in Column 1 by controlling for the interactions between the 

provincial characteristics 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). The results are 

robust when we use IPP2 to measure IP enforcement (Column 4 and Column 5). The results do not 

change qualitatively in the instrumental variable regressions (Column 3 and Column 6). 

4.3 The effect of IP rights enforcement on R&D output 

We now investigate the impact of IP rights enforcement on R&D output. The first measure of 

R&D output is the number of innovation patents. The number of innovation patents is predicted to 

27

e affected by IP rights enforcement: firms in provinces 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 for dealing with a discrete non-negative dependent variable is the Poisson 

regression model. The number of innovation patents created by firm i in year t, P it , is assumed to be 

independent under a Poisson distribution with the parameter λ it . λ it depends on a set of explanatory 

variables which are the determinants of patent creation. The model is specified as follows: 

ln(λ )= β + β ln(R & D Stock) + β IP Rights Enforcement + β ln(Total Assets) 

i,t 0 1 i,t 2 i,t -1 3 i,t -1 

+ β ln(Age) + β Industry Dummies + 

4 i,t-1 5 i,t 

(3) 

R&D stock is computed from the standard exponentially declining formula for capital stocks, 

kit , 

(1 

) kit , 1 rit 

, 

, where k it , 

is the end-of-period stock of R&D capital and r it , 

is the R&D 

expenditure during year t (Crepon and Duguet, 1997; and Hall, Jaffe, and Trajtenberg, 2005). The 

depreciation rate is set to be 15%, which is generally adopted by prior literature. 16 

Our sample 

period is only five years, thus we choose to include two lagged R&D expenditures in calculating 

R&D stock, which reduces our firm-year observations in regression to 14,374. 


Table 6 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 innovation patents. In Column 3 and 6, we estimate 

the regression model of innovation patents under the instrumental variable approach, and the 

coefficient of IPP1 and IPP2 is still statistically significant and positive. The results are consistent 

16 The choice of makes little difference to our result, because our sample period is only five years. 

28

with the hypothesis that strong IP rights enforcement encourages the production of patents. 

The second measure of R&D output is the ratio of new product sales to total sales. Poor IP 

rights enforcement could adversely affect new product introduction and subsequent sales figures. 

We use the OLS model to estimate the effect of IP rights enforcement on new product sales. 

New Product Sale Ratio = α+ β IP Rights Enforcement 

+ β Provincial Variables 

i,t 1 i,t-1 2 i,t-1 

+ β Firm Variables + β Industry Dummies + ε 

3 i,t-1 4 i i,t 

(4) 

Column 1 of Table 7 shows that after controlling for 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 2.9 percentage points. This effect is still highly significant when controlling for provincial 

characteristics (Column 2). The magnitude is even greater; a one-standard-deviation increase in the 

IP rights enforcement is associated with an increase of 5.2 percentage points in the new product 

sales ratio. Our results are robust to the use of the instrumental variables (Column 3). We also use 

different specifications of R&D input by replacing the average three-year R&D intensity for the one 

year lagged R&D intensity, and the results still hold (not tabulated). The results are robust when 

IPP2 is used to measure IP enforcement. 


The results in Table 6 and Table 7 are consistent with Hypothesis 4A and Hypothesis 4B: better 

IP rights enforcement increases patents obtained by firms and increases firms’ sales of new products. 

5. Conclusions 

Now we have an answer to the question raised 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 

29

intellectual properties? By examining IP rights enforcement in different provinces within the same 

country, we find that enforcement of intellectual property protection does matter in China. High tech 

firms in provinces with better IP rights enforcement have greater access to external debt (formal and 

informal). They invest more funding in R&D, and have better tangible results, i.e., more patents and 

new product sales. To deal with the problem of endogeneity, we employ the instrumental variable 

approach by using the number of Christian colleges by 1920 and the presence of British concessions 

in the Qing dynasty as the instruments. The impact of IP rights enforcement is robust to different 

specifications. 

Our paper suggests that the enforcement of IP rights can contribute to economic growth through 

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 enforcement varies across provinces. International 

differences in the enforcement of IP rights must await future efforts. 

30

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34

Licensing fees Paid by Chinese Companies to Foreign 

Countries (Billion US$) 

30 

25 

20 

15 

10 

5 

0 

2004 2005 2006 2007 2008 

Figure 1 

The licensing fees paid by Chinese companies to foreign countries from 2004 to 2008. The data comes from the 

Ministry of Commerce of China. 

Beijing 

Shanghai 

The Percentage of IP infringement cases won by plaintiffs during 2001 to 2005 

0.8 or more 0.67 to 0.80 

0.50 to 0.67 0.25 to 0.50 

Figure 2 

Intellectual property rights enforcement across Chinese provinces: The percentage of IP infringement cases won by 

plaintiffs during 2001-2005. 

35

Table 1: Summary statistics and correlations 

Panel A reports summary statistics of provincial-level and firm-level variables. IPP1 is the percentage of IP infringement cases won by 

plaintiffs during 2001-2005, and IPP2 is the number of articles advocating IP protection published in the official newspaper of 

provincial Communist Party in each year for each province. For a description of all the other variables, see Appendix 3. Panel B shows 

the correlation coefficients among the measures of provincial IP rights enforcement and other provincial-level variables. *** , ** , * in 

Panel B indicate that the correlation coefficient is statistically significant at the 1-, 5-, and 10-percent level, respectively. 

Panel A: Summery Statistics 

variable Mean S. D. Min 

25th 

percentile 

Median 

75th 

percentile 

IPP1 0.724 0.116 0.250 0.682 0.765 0.779 0.875 23955 

IPP2 8.535 6.464 0 3 5 15 19 21783 

Christian Colleges 1.421 0.942 0 1 2 2 3 23955 

British Settlement 0.682 0.466 0 0 1 1 1 23955 

Access to New Debt 0.472 0.499 0 0 0 1 1 23955 

Access to New External Equity 0.184 0.387 0 0 0 0 1 23955 

Informal Financing Ratio 0.285 0.709 -2.991 0.375 0.462 0.619 2.291 6487 

R&D Intensity 0.076 0.130 0 0.006 0.032 0.084 0.832 23821 

Number of Innovation Patents 0.812 22.643 0 0 0 0 2254 23955 

New Product Sales Ratio 0.213 0.344 0 0 0 0.346 1 23663 

Corruption Control 7.486 2.072 1.765 6.510 7.240 8.915 12.350 23955 

Banking Development 6.519 1.805 0.850 5.550 6.390 7.320 11.070 23955 

GDP Growth 0.120 0.019 0.068 0.110 0.117 0.137 0.209 23955 

University 65.945 18.272 9 58 66 77 112 23955 

Metropolis 0.904 0.294 0 1 1 1 1 23955 

Patent Dummy 0.209 0.406 0 0 0 0 1 23955 

Sales Growth 1.335 4.610 -0.954 -0.108 0.183 0.648 24.680 23955 

Intangible to Total Assets Ratio 0.052 0.114 0 0 0.001 0.052 0.734 23955 

ROA 0.053 0.116 -0.276 0 0.026 0.089 0.532 23955 

Leverage 0.464 0.266 0.000 0.257 0.462 0.656 1.095 23955 

New Debt/Assets 0.115 0.331 -0.586 -0.041 0.044 0.200 1.690 23821 

New External Equity/Assets 0.034 0.240 -0.548 -0.023 0 0.018 1.344 23821 

New Internal Finance/Assets 0.046 0.132 -0.276 0 0.007 0.071 0.707 23821 

Log (R&D Stock) 8.462 1.815 -0.325 7.431 8.537 9.591 14.803 14374 

Log(Assets) 10.962 1.313 9.210 9.928 10.691 11.742 17.509 23955 

Log(Firm Age) 1.766 0.762 0 1.386 1.792 2.303 4.477 23955 

Panel B: Correlation matrix of IP rights enforcement and provincial-level variables 

IPP1 

IPP1 1 

IPP2 

Christian 

Colleges 

British Corruption 

Settlement Control 

Banking 

Development 

GDP 

Growth 

Max 

University 

Obs. 

Metropolis 

IPP2 0.258*** 1 

Christian Colleges 0.199*** 0.424*** 1 

British Settlement 0.229*** 0.557*** 0.546*** 1 

Corruption Control 0.170*** 0.507*** 0.297*** 0.365*** 1 

Banking Development 0.291*** 0.237*** 0.301*** 0.335*** 0.646*** 1 

GDP Growth 0.235*** 0.231*** 0.148*** 0.340*** 0.731*** 0.611*** 1 

University 0.062*** 0.312*** 0.582*** 0.436*** 0.491*** 0.491*** 0.427*** 1 

Metropolis 0.138*** 0.300*** 0.272*** 0.422*** 0.209*** 0.352*** 0.240*** 0.302*** 1 

36

Table 2: The effect of IP rights enforcement on access to new debt 

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. For a description of all the other variables, see Appendix 3. All regressions 

include industry dummies as part of the control variables. All the independent variables are lagged by one year. For Columns (1), 

(2), (4) and (5), the reported coefficients are probit estimates of the effect of marginal change in the corresponding regressors on 

the probability of access to new debt, computed at the sample mean of the independent variables. Column (3) and (6) present 

results obtained from the 2SLS regression, with the instruments being the provincial number of Christian colleges by 1920 

(Christian Colleges) and the dummy which equals 1 when a province had a British concession during the late Qing Dynasty 

(British Settlement). Other covariates in the first-stage regression are omitted for presentational convenience. The numbers in the 

parentheses are z-stat based on standard errors adjusted for heteroskedasticity and the potential clustering of the residual at the 

provincial level. ***, **, * indicate the coefficient is statistically different from zero at the 1-, 5-, and 10-percent level, respectively. 

(1) (2) (3) (4) (5) (6) 

Model Probit Probit 

2SLS: Second 

2SLS: Second 

Probit Probit 

stage 

stage 

Dependent variables 

Access to Access to Access to new Access to Access to Access to new 

new debt new debt debt new debt new debt debt 

IPP1 0.127 0.109 0.318 

(4.45)*** (3.56)*** (2.11)** 

IPP2 0.001 0.002 0.004 

(2.22)** (2.22)** (2.81)*** 

Corruption Control -0.003 -0.002 -0.009 -0.013 

(-1.23) (-0.57) (-2.74)*** (-3.33)*** 

Banking Development 0.004 -0.001 0.004 0.006 

(1.32) (-0.32) (1.54) (2.07)** 

GDP Growth 0.208 -0.008 0.750 0.795 

(0.77) (-0.03) (2.43)** (2.60)*** 

University 0.0003 0.0003 0.0004 0.0003 

(1.46) (1.99)** (1.83)* (1.52) 

Metropolis -0.003 -0.003 -0.001 -0.013 

(-0.22) (-0.28) (-0.06) (-0.89) 

Patent Dummy 0.037 0.036 0.038 0.036 0.036 0.036 

(4.50)*** (4.36)*** (4.58)*** (4.21)*** (4.17)*** (4.21)*** 

R&D Intensity 0.219 0.216 0.216 0.226 0.563 0.218 

(6.78)*** (6.68)*** (6.84)*** (6.74)*** (6.67)*** (6.67)*** 

Sales Growth 0.006 0.006 0.006 0.006 0.006 0.005 

(7.74)*** (7.74)*** (7.44)*** (7.25)*** (7.23)*** (7.13)*** 

Intangible/TA -0.103 -0.104 -0.100 -0.110 -0.108 -0.105 

(-3.43)*** (-3.44)*** (-3.41)*** (-3.53)*** (-3.47)*** (-3.49)*** 

ROA 0.068 0.063 0.066 0.065 0.060 0.063 

(2.29)** (2.14)** (2.26)** (2.10)** (1.94)* (2.09)** 

Leverage -0.052 -0.053 -0.051 -0.050 -0.051 -0.049 

(-3.90)*** (-3.98)*** (-3.94)*** (-3.57)*** (-3.69)*** (-3.59)*** 

Log(assets) -0.003 -0.003 -0.002 -0.002 -0.002 -0.001 

(-0.97) (-1.16) (-0.89) (-0.54) (-0.75) (-0.49) 

Log(age) -0.058 -0.059 -0.055 -0.064 -0.064 -0.062 

(-12.22)*** (-12.26)*** (-10.87)*** (-12.84)*** (-12.67)*** (-12.58)*** 

Observations 23,955 23,955 23,955 21,783 21,783 21,783 

Pseudo R 2 / R 2 1.72% 1.74% 2.18% 1.83% 1.89% 2.59% 

First Stage 

Regression 

First Stage 

Regression 

Dependent variables IPP1 IPP2 

Christian Colleges 0.025 0.545 

(21.14)*** (11.43)*** 

British Settlement 0.014 4.698 

(6.49)*** (53.57)*** 

Observations 23,955 21,783 

R-squared 19.05% 52.89% 

Partial F-test of IVs 513.49(p=0.00) 2,636.71(p=0.00) 

J stat. from 

overidentification test 

0.045(p=0.832) 2.239 (p=0.135) 

37

Table 3: The effect of IP rights enforcement on new external equity 

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. For a description of all the other variables, see 

Appendix 3. All regressions include industry dummies as part of the control variables. All the independent variables are 

lagged by one year. For Columns (1), (2), (4) and (5), the reported coefficients are probit estimates of the effect of 

marginal change in the corresponding regressors on the probability of raising new external equity, computed at the sample 

mean of the independent variables. Column (3) and (6) present results obtained from the 2SLS regression, with the 

instruments being the provincial number of Christian colleges by 1920 (Christian Colleges) and the dummy which equals 1 

when a province had a British concession during the late Qing Dynasty (British Settlement). Other covariates in the firststage 

regression are omitted for presentational convenience. The numbers in the parentheses are z-stat based on standard 

errors adjusted for heteroskedasticity and the potential clustering of the residual at the provincial level. ***, **, * indicate 

the coefficient is statistically different from zero at the 1-, 5-, and 10-percent level, respectively. 

(1) (2) (3) (4) (5) (6) 

Model Probit Probit 

2SLS: Second 

2SLS: Second 

Probit Probit 

stage 

stage 


New external New external New external New external New external New external 

equity equity equity equity equity equity 

IPP1 -0.051 -0.046 0.075 

(-1.46) (-1.76)* (0.39) 

IPP2 -0.002 -0.003 -0.001 

(-4.02)*** (-7.08)*** (-0.48) 

Corruption Control -0.002 -0.001 0.003 -0.001 

(-0.64) (-0.25) (0.90) (-0.15) 

Banking Development -0.0001 -0.003 -0.003 -0.002 

(-0.05) (-0.42) (-1.91)* (-0.99) 

GDP Growth -0.062 -0.186 -0.287 -0.082 

(-0.21) (-0.42) (-0.86) (-0.19) 

University 0.0004 0.001 0.001 0.001 

(1.80)* (1.39) (2.88)*** (2.29)** 

Metropolis -0.001 -0.001 0.015 0.007 

(-0.11) (-0.09) (1.87)* (0.58) 

Patent Dummy 0.010 0.011 0.012 0.010 0.010 0.011 

(1.30) (1.36) (1.44) (1.18) (1.25) (1.30) 

R&D Intensity 0.017 0.018 0.022 0.024 0.026 0.027 

(0.87) (0.92) (1.13) (1.25) (1.35) (1.23) 

Sales Growth 0.004 0.004 0.004 0.004 0.004 0.004 

(3.38)*** (3.63)*** (3.90)*** (3.44)*** (3.46)*** (3.48)*** 

Intangible/TA 0.105 0.104 0.109 0.092 0.091 0.094 

(3.58)*** (3.63)*** (3.80)*** (4.02)*** (4.01)*** (3.99)*** 

ROA -0.024 -0.026 -0.028 -0.029 -0.031 -0.032 

(-0.86) (-0.91) (-1.00) (1.02) (-1.01) (-1.11) 

Leverage 0.169 0.168 0.174 0.161 0.161 0.167 

(6.30)*** (6.27)*** (6.78)*** (6.74)*** (6.72)*** (6.84)*** 

Log(assets) -0.016 -0.017 -0.016 -0.017 -0.017 -0.017 

(-5.13)*** (-5.00)*** (-5.32)*** (-5.11)*** (-5.02)*** (-5.03)*** 

Log(age) -0.027 -0.026 -0.026 -0.027 -0.026 -0.026 

(-3.27)*** (-3.17)*** (-2.62)*** (-2.95)*** (-2.91)*** (-2.84)*** 

Observations 23,955 23,955 23,955 21,783 21,783 21,783 

Pseudo R 2 2.36% 2.39% 2.24% 2.31% 2.40% 2.31% 

First Stage 

Regression 

First Stage 

Regression 



(21.14)*** (11.43)*** 


(6.49)*** (53.57)*** 


R-squared 19.05% 52.89% 

Partial F-test of IVs 513.49(p=0.00) 2636.71(p=0.00) 

J stat. from 


2.040(p=0.153) 2.576(p=0.109) 

38

Table 4: The effect of IP rights enforcement on informal financing 

The dependent variable is the proportion of informal financing (debt minus bank loan and trade credit) in the newly raised debt. 

Only firms that raised new debt exceeding 5% of their total assets in 2004 and 2005 enter the regressions, because the data on bank 

loan is available only in these two years. For a description of all the other variables, see Appendix 3. All regressions include 

industry dummies as part of the control variables. All the independent variables are lagged by one year. The coefficients reported 

in Column (1), (2), (4) and (5) are from OLS regressions. Column (3) and (6) present results obtained from 2SLS regressions, with 

the instruments being the provincial number of Christian colleges till 1920 (Christian Colleges) and the dummy which equals 1 

when a province had a British concession during the late Qing Dynasty (British Settlement). Other covariates in the first-stage 

regression are omitted for presentational convenience. The numbers in the parentheses are t-stat based on standard errors adjusted 

for heteroskedasticity and the potential clustering of the residual at the provincial level. ***, **, * indicate the coefficient is 

statistically different from zero at the 1-, 5-, and 10-percent level, respectively. 

(1) (2) (3) (4) (5) (6) 

2SLS: Second 

2SLS: Second 

Model OLS OLS 

OLS OLS 

stage 

stage 


Informal 

financing 

ratio 

Informal 


ratio 

Informal 


ratio 

Informal 


ratio 

Informal 


ratio 

Informal 


ratio 

IPP1 0.220 0.187 1.099 

(2.27)** (1.80)* (2.69)*** 

IPP2 0.020 0.021 0.013 

(15.15)*** (10.20)*** (3.18)*** 

Corruption Control 0.065 0.076 0.031 0.044 

(7.56)*** (7.56)*** (3.26)*** (4.03)*** 


(-2.91)*** (-3.57)*** (-4.07)*** (-3.55)*** 

GDP Growth 1.265 1.414 2.774 2.224 

(5.20)*** (5.42)*** (9.01)*** (5.73)*** 

University 0.006 0.005 0.003 0.005 

(7.38)*** (5.11)*** (3.64)*** (4.14)*** 

Metropolis -0.062 -0.029 -0.006 -0.030 

(-4.80)*** (-1.40) (-0.40) (-1.65)* 

Patent Dummy -0.018 -0.019 -0.008 -0.022 -0.022 -0.025 

(-0.81) (0.88) (-0.39) (-1.03) (-1.06) (1.17) 

R&D Intensity 0.091 0.061 0.090 0.086 0.096 0.095 

(1.40) (0.94) (1.36) (1.35) (1.51) (1.49) 

Sales Growth 0.004 0.002 0.002 0.002 0.002 0.002 

(2.68)*** (1.76)* (1.33) (1.26) (1.12) (1.41) 

Intangible/TA -0.165 -0.215 -0.217 -0.162 -0.206 -0.205 

(-2.78)*** (-3.69)*** (-3.74)*** (-2.88)*** (-3.68)*** (-3.65)*** 

ROA 0.011 0.022 0.018 0.043 0.023 0.019 

(0.19) (0.40) (0.33) (0.76) (0.41) (0.35) 

Leverage -0.141 -0.134 -0.133 -0.122 -0.135 -0.134 

(-4.61)*** (-4.47)*** (-4.41)*** (-4.07)*** (-4.50)*** (-4.48)*** 

Log(assets) -0.075 -0.072 -0.069 -0.064 -0.064 -0.066 

(-9.79)*** (-9.56)*** (-9.03)*** (-8.52)*** (-8.58)*** (-8.75)*** 

Log(age) 0.002 0.002 0.011 0.021 0.012 0.010 

(0.12) (0.17) (0.83) (1.60) (0.94) (0.74) 

Constant 1.080 0.180 -0.349 0.882 0.264 0.260 

(7.35)*** (1.19) (1.24) (6.84)*** (1.81)* (1.77)* 

Observations 6,487 6,487 6,487 6,125 6,125 6,125 

R-squared 6.60% 11.13% 9.56% 9.71% 13.06% 12.82% 

First Stage 

Regression 

First Stage 

Regression 



(5.94)*** (6.21)*** 


(17.32)*** (31.14)*** 


R-squared 26.67% 73.14% 

Partial F-test of IVs 202.75 (p=0.00) 1,124.34 (p=0.00) 

J stat. from 


1.284 (p=0.257) 2.717 (p=0.100) 

39

Table 5: The 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 total 

assets. For a description of all the other variables, see Appendix 3. All regressions include industry dummies as control 

variables. All independent variables are lagged by one year, except the funding source variables. The coefficients reported 

in Column (1), (2), (4) and (5) are from OLS regressions. Column (3) and (6) present results obtained from the IV 

regressions, with the instruments being the provincial number of Christian colleges by 1920 (Christian Colleges) and the 

dummy which equals 1 when a province had a British concession during the late Qing Dynasty (British Settlement). Other 

covariates in the first-stage regression are omitted for presentational convenience. The numbers in the parentheses are t-stat 

based on standard errors adjusted for heteroskedasticity and the potential clustering of the residual at the provincial level. 

***, **, * indicate the coefficient is statistically different from zero at the 1-, 5-, and 10-percent level, respectively. 

Model OLS OLS 


(1) (2) (3) (4) (5) (6) 

2SLS: Second 

stage 

OLS OLS 

R&D 

Intensity 

R&D 

Intensity 

R&D Intensity 

R&D 

Intensity 

R&D 

Intensity 

2SLS: Second 

stage 

R&D Intensity 

New Debt/Assets 0.009 0.009 0.006 0.021 0.021 0.019 

(0.71) (0.72) (0.49) (1.65)* (1.66)* (1.23) 

New External Equity/Assets 0.009 0.009 0.006 0.021 0.021 0.019 

(0.71) (0.72) (0.50) (1.65)* (1.66)* (1.23) 

New Internal Finance/Assets 0.046 0.046 0.059 0.075 0.074 0.067 

(2.62)*** (2.60)*** (2.71)*** (3.78)*** (3.71)*** (3.40)*** 

IPP1×New Debt/Assets 0.087 0.088 0.103 

(5.02)*** (5.05)*** (5.40)*** 

IPP1×New External Equity/Assets 0.028 0.029 0.034 

(0.99) (1.03) (1.17) 

IPP1×New Internal Financing/Assets 0.183 0.181 0.141 

(5.38)*** (5.27)*** (3.19)*** 

IPP1 0.022 -0.019 0.073 

(0.83) (-0.82) (1.01) 

IPP2×New Debt/Assets 0.005 0.005 0.006 

(5.71)*** (5.77)*** (4.23)*** 

IPP2×New External Equity/Assets 0.001 0.001 0.002 

(0.73) (0.76) (0.70) 

IPP2×New Internal Financing/Assets 0.007 0.007 0.009 

(3.16)*** (3.14)*** (3.02)*** 

IPP2 -0.0001 -0.001 0.001 

(-0.16) (2.56)** (1.14) 

Corruption Control 0.001 0.002 0.001 -0.002 

(0.37) (1.31) (0.54) (-0.43) 

Banking Development 0.006 0.002 0.006 0.005 

(3.88)*** (1.07) (3.65)*** (3.32)*** 

GDP Growth 0.369 0.237 0.267 0.451 

(1.74)* (1.24) (0.88) (1.17) 

University -0.0002 -0.0002 -0.0002 -0.001 

(-0.51) (-0.77) (-1.05) (-1.55) 

Metropolis -0.004 -0.001 -0.003 0.003 

(-2.45)** (-0.34) (-2.04)** (0.77) 

Constant 0.017 -0.025 -0.069 0.032 -0.022 -0.012 

(0.85) (-1.07) (-1.62) (4.36)*** (1.29) (-0.73) 

Observations 23,821 23,821 23,821 21,655 21,655 21,655 

R-squared 1.76% 1.89% 1.81% 1.69% 1.80% 1.70% 

First Stage 

Regression 

First Stage 

Regression 


40


(14.91)*** (2.23)** 


(11.74)*** (63.08)*** 

Christian Colleges 

0.006 0.106 

×NewDebt/Assets (3.05)*** (0.93) 

British Settlement 

-0.001 0.277 

×New Debt/Assets (-0.33) (1.13) 


0.001 0.037 

×New External Equity/Assets (0.33) (0.21) 


0.0002 -0.335 

×New External Equity/Assets (0.03) (-0.93) 


-0.003 0.156 

×New Internal Financing/Assets (-0.73) (0.56) 


0.026 -0.538 

×New Internal Financing/Assets (2.54)** (-0.87) 


R-squared 23.03% 63.85% 

Partial F-test of IVs 162.57(p=0.00) 1,142.67(p=0.00) 

J stat. from overidentification test 4.670 (p=0.32) 5.820 (p=0.21) 

41

Table 6: The effect of IP rights enforcement on patent output 

The dependent variable is the number of innovation patents. For a detailed description of all variables, see Appendix 3. All 

regressions include industry dummies as control variables. All independent variables are lagged by one year. The reported 

coefficients in Column (1), (2), (4) and (5) are Poisson regressions. Column (3) and (6) present results obtained from the IV 

regressions, with the instruments being the provincial number of Christian colleges by 1920 (Christian Colleges) and the dummy 

which equals 1 when a province had a British concession during the late Qing Dynasty (British Settlement). Other covariates in the 

first-stage regression are omitted for presentational convenience. The numbers in the parentheses are z-stat (t-stat) based on 

standard errors adjusted for heteroskedasticity and the potential clustering of the residual at the provincial level. ***, **, * indicate 

the coefficient is statistically different from zero at the 1-, 5-, and 10-percent level, respectively. 

(1) (2) (3) (4) (5) (6) 

Model Poisson Poisson 

IV-Second 

IV-Second 

Poisson Poisson 

stage: Poisson 

stage: Poisson 

Dependent variables Patent Patent Patent Patent Patent Patent 

IPP1 9.550 4.530 9.748 

(1.73)* (2.50)** (2.64)*** 

IPP2 0.339 0.310 0.373 

(2.69)*** (6.17)*** (3.30)*** 

Corruption Control -0.076 -0.071 -0.203 -0.286 

(-0.96) (-0.98) (-1.13) (-1.03) 

Banking Development -0.110 -0.403 0.009 -0.104 

(-1.11) (-2.16)** (0.09) (-0.71) 

GDP Growth 4.521 6.151 3.355 5.181 

(2.30)** (2.27)** (1.74)* (1.80)* 

University -0.032 -0.049 -0.008 -0.011 

(-1.11) (-1.64) (-1.01) (-1.21) 

Metropolis 0.727 1.398 0.581 1.104 

(1.96)** (2.83)*** (1.33) (1.89)* 

Log(R&D Stock) 0.903 0.949 0.955 0.906 0.896 0.907 

(7.08)*** (6.78)*** (6.38)*** (4.65)*** (7.81)*** (7.30)*** 

Log(Assets) 0.180 0.097 0.100 0.106 0.152 0.135 

(1.65)* (1.10) (1.21) (1.54) (1.84)* (1.60) 

Log(Firm Age) -0.069 -0.120 -0.036 -0.104 -0.209 -0.133 

(-0.32) (-0.57) (0.18) (0.46) (1.07) (-0.63) 

Constant -20.269 -13.687 -16.205 -14.573 -13.769 -13.995 

(-3.21)*** (-4.89)*** (-4.68)*** (-5.09)*** (-7.63)*** (-7.55)*** 

Observations 14,374 14,374 14,374 13,316 13,316 13,316 

Wald chi2 (Prob>chi2) 14538.37 

(P=0.00) 

23648.82 

(P=0.00) 

26924.07 

(P=0.00) 

19120.57 

(P=0.00) 

62345.51 

(P=0.00) 

54320.06 

(P=0.00) 

First Stage: 

OLS 

First Stage: 

OLS 



(16.31)*** (14.87)*** 


(7.28)*** (47.86)*** 


R-squared 18.97% 58.52% 

Partial F-test of IVs 319.92 (p=0.00) 2670.00 (p=0.00) 

42

Table 7: The effect of IP rights enforcement on new product sales 

The dependent variable is new product sales divided by total sales. For a detailed description of all variables, see Appendix 

3. All regressions include industry dummies as control variables. All independent variables are lagged by one year. The 

reported coefficients in Column (1), (2), (4) and (5) are from OLS regressions. Column (3) and (6) present results obtained 

from the 2SLS regressions, with the instruments being the provincial number of Christian colleges by 1920 (Christian 

Colleges) and the dummy which equals 1 when a province had a British concession during the late Qing Dynasty (British 

Settlement). Other covariates in the first-stage regression are omitted for presentational convenience. The numbers in the 

parentheses are t-stat based on standard errors adjusted for heteroskedasticity and the potential clustering of the residual at 

the provincial level. ***, **, * indicate the coefficient is statistically different from zero at the 1-, 5-, and 10-percent level, 

respectively. 

Model OLS OLS 


(1) (2) (3) (4) (5) (6) 

2SLS: Second 

stage 

OLS OLS 

New 

Product 

Sales 

New Product 

Sales 

New Product 

Sales 

New 

Product 

Sales 

New Product 

Sales 

2SLS: Second 

stage 

New Product 

Sales 

IPP1 0.251 0.444 2.503 

(1.83)* (4.13)*** (2.55)** 

IPP2 0.016 0.017 0.027 

(5.60)*** (9.75)*** (5.16)*** 

Corruption Control 0.045 0.060 -0.001 -0.023 

(3.75)*** (3.49)*** (-0.20) (-1.54) 


(-5.79)*** (-3.93)*** (-5.15)*** (-4.82)*** 

GDP Growth -0.742 -2.760 2.016 3.149 

(-0.70) (-1.09) (2.16)** (2.18)** 

University 0.001 0.003 0.0003 0.0001 

(1.75)* (1.56) (0.55) (0.14) 

Metropolis 0.146 0.138 0.050 0.003 

(4.59)*** (1.45) (1.89)* (0.10) 

R&D Intensity 0.127 0.152 0.191 0.093 0.125 0.119 

(4.64)*** (6.09)*** (4.51)*** (2.91)*** (5.21)*** (4.68)*** 

Patent Dummy 0.059 0.064 0.089 0.051 0.060 0.062 

(4.46)*** (5.67)*** (6.32)*** (3.62)*** (4.84)*** (5.00)*** 

Log(assets) -0.005 0.001 0.008 -0.002 0.003 0.005 

(-0.67) (0.18) (1.12) (-0.28) (0.45) (0.97) 

Log(age) -0.015 -0.006 0.024 -0.010 -0.004 0.001 

(-3.63)*** (-1.54) (1.52) (-1.80)* (-1.05) (0.20) 

Constant 0.152 -0.082 -1.369 0.173 0.103 0.043 

(2.31)** (-0.91) (-2.17)** (2.30)** (1.52) (0.56) 

Observations 23,663 23,663 23,663 21,500 21,500 21,500 

R-squared 3.57% 10.65% 11.11% 10.99% 14.09% 12.35% 

First Stage 

Regression 

First Stage 

Regression 



(21.59)*** (10.08)*** 


(6.73)*** (46.20)*** 


R-squared 18.87% 52.47% 

Partial F-test of IVs 478.85 (p=0.00) 2321.33 (p=0.00) 

J stat. from 


0.363 (p=0.547) 2.134 (p=0.144) 

43

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 

Trademark Law of the People’s Republic of China 

First amendment Date: February 22, 1993 

Second amendment Data: October 27, 2001 

Effective date: April 1, 1985 

Patent Law of the People’s Republic of China 

First amendment Date: September 4, 1992 

Second amendment Data: August 25, 2000 

Effective date: June 1, 1991 

Copyright Law of the People’s Republic of China 

First amendment Date: October 27, 2001 

Effective date: May 8, 1997 

Rules for Pesticide Administration 

First amendment Date: November 29, 2001 


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 

Implementing Regulations on Patent Law 

First amendment Date: December 28, 2002 

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 


Regulations on Protection of the Olympic Symbols 


Management Measures of Wholesale, Retail, and Rent of 

Audiovisual Production 


Management Measures of Audiovisual Production Import 

Effective date: September 15, 2002 

Implementing Regulations on the Copyright Law 


Implementing Regulations on Trademark Law 


Regulations for the Implementation of Drug Administration 

Law 


Provisions for Identification and Protection of Well-known 

Trademarks 


Procedure for the Registration and Administration of 

Collective Marks and Certification Marks 


Measures on Compulsory Licensing of Patents 


Measures for Enforcement of Copyright Administration 

Penalty 


Measures of the Implementation of Regulations Governing 

Customs Protection of Intellectual Property Rights 


Regulations on the Customs Protection of Intellectual 

Properties 

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 Properties 


Regulations on the Copyright Collective Administration 

Source: State Intellectual Property Office of China (SIPO) 

44

Appendix 2: 

List of International Conventions/Treaties on Intellectual Property Rights that China has Acceded to 

Date of Accession 

Since June 3, 1980, China has been a member state of 

World Intellectual Property Organization 

Since March 19,1985, a member state of Paris Convention 

Since 1989, one of the first member states 

Since October 4, 1989, a member state of Madrid 

Agreement 

Since October 15, 1992, a member state of Bern 

Convention 

Since October 30, 1992, a member state of Universal 

Copyright Convention 

Since April 30, 1993, a member state of the Convention 

Since January 1, 1994, a member state of the Convention 

Since August 9, 1994, a member state of the Nice 

Agreement 

Since July 1, 1995, a member state of the Budapest Treaty 

Since September 19, 1996, a member state of the Locarno 

Agreement 

Since June 19, 1997, a member state of the Strasbourg 

Agreement 

Since April 23, 1999, a member state of UPOV 

Since December 11, 2001, a member state of the 

Agreement 

Source: State Intellectual Property Office of China (SIPO) 

Name of Treaty 

Convention Establishing the World Intellectual Property 

Organization 

Paris Convention for the Protection of Industrial Property 

Treaty on Intellectual Properties in Respects of Integrated 

Circuits 

Madrid Agreement Concerning the International 

Registration of Marks 

Bern Convention for the Protection of Literary and Artistic 

Works 

Universal Copyright Convention 

Convention for the Protection of Producers of Phonograms 

against Unauthorized Duplication of their Phonograms 

Patent Cooperation Treaty 

Nice Agreement Concerning the International 

Classification of Goods and Service for the Purposes of the 

Registration of Marks 

Budapest Treaty on the International Recognition of the 

Deposit of Microorganisms for the Purposes of Patent 

Procedure 

Locarno Agreement Establishing an International 

Classification for Industrial Design 

Strasbourg Agreement Concerning the International Patent 

Classification 

International Convention for the Protection of New 

Varieties of Plants 

Agreement of World Trade Organization on Trade-related 

Aspects of Intellectual Property Rights 

45

Appendix 3: 

Definition of the Variables 

Variable Description Source 

IPP1 

The number of IP infringement cases won by the plaintiffs divided by the sum 

of cases won by plaintiffs and cases won by defendants. We identify that a 

case is won by plaintiff if the court orders the defendant to cease IP 

infringement, compensate the plaintiff for its loss due to the infringement, 

destruct the infringing products and equipments involved, and/or pay the legal 

cost of the lawsuit. The case is won by the defendant if the court dismisses the 

lawsuit of the plaintiff. 

IPP2 

Number of articles advocating IP protection in the newspaper published by 

provincial Community Party, which is the highest organ of the provincial 

administration. This variable has province-year observations from 2001-2005 

for 27 provinces which does not include Shandong province because of 

missing observations in the database. The newspaper published by provincial 

committee of CCP in each province are (name of province in parenthesis): 

Anhui Daily (Anhui); Beijing Daily (Beijing); Chongqing Daily (Chongqing); 

Fujian Daily (Fujian); Gansu Daily (Gansu); Nanfang Daily (Guangdong); 

Guangxi Daily (Guangxi); Guizhou Daily (Guizhou); Hainan Daily (Hainan); 

Hebei Daily (Hebei); Heilongjiang Daily (Heilongjiang); Henan Daily 

(Henan); Hubei Daily (Hubei); Hunan Daily (Hunan); Xinhua Daily (Jiangsu); 

Jiangxi Daily (Jiangxi); Jilin Daily (Jilin); Liaoning Daily (Liaoning); 

Neimenggu Daily (Neimenggu); Dazhong Daily (Shandong); Jiefan Daily 

(Shanghai); Shaanxi Daily (Shaanxi); Shanxi Daily (Shanxi); Sichuan Daily 

(Sichuan); Tianjin Daily (Tianjin); Xinjiang Daily (Xinjiang); Yunnan Daily 

(Yunnan); Zhejiang Daily (Zhejiang). 

46 

Judicial Case 

Database of 

Chinalawinfo 

Chinese Major 

Newspapers Fulltext 

Database 

Christian Colleges The provincial number of Christian colleges by 1920. Stauffer, Wong and 

Tewksbury (1922) 

British Settlement Dummy variable equals 1 when a province had a British concession or leased 

territory during the late Qing Dynasty, 0 otherwise. The concession or leased 

Yang & Ye (1993) 

territory of British were in Xiamen city in Fujian, Hankou city in Hubei, 

Jiujiang city in Jiangxi, Zhenjiang City in Jiangsu, Guangzhou City in 

Guangdong, Weihaiwei in Shandong, Tianjin, and Shanghai. 

New Product Sales 

Ratio 

University The number of universities in a province. NBS 

Metropolis Dummy variable equals 1 if the province has at least one city with over two 

million inhabitants. 

NBS 

Access to New Dummy variable equals 1 if there is a net increase of debt for firm i in year t MOST 

Debt 

that exceeds 5% of its total assets at the end of year t. 

Informal Financing It’s the net increase in informal financing (debt minus bank loan and trade MOST 

Ratio 

credit) for firm i in year t as a percentage of new debt of year t. We only 

construct this variable for firms who had access to new debt in 2004 and 2005, 

because the data of bank loan is only available in these two years. MOST 

dataset does not disclose trade credit, so we use instead, the industry median 

ratio of new trade credit to the net increase of all debt for the listed companies 

in the same industry-year, in the calculation of informal financing ratio. The 

classification of industry refers to Industry Classification Standard from the 

CSRC (China Securities Regulatory Commission). 

R&D Intensity Research and development expenditure of firm i in year t divided by start-of- 

MOST 

year of book asset. 

New product sales divided by total sales. 292 observations are coded as 

missing, because they have zero total sales at that observing point. 

GDP Growth Annual growth rate of provincial GDP using PPP. NBS 

Banking 

Development 

Corruption Control 

Provincial banking system development index is the arithmetic average of the 

standardized value of following two sub-indexes: first, the competition in the 

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 nonstate 

sector for each province. Standardized value is calculated according to 

following formula: Score = (V i -V min )/(V max -V min )×10 , where V i is the 

original score of index i in the period of 2001 to 2005; V max and V min are the 

maximum and minimum of the original score of all provinces in base year 

(2001). 

Provincial Corruption Control index is the arithmetic average of the 

standardized value of following two sub-indexes: first, intervention by the 

government in business operations, measured as the time spent by 

MOST 

Fan& Wang (2001, 

2004,2007, 2010) 

Fan& Wang (2001, 

2004, 2007, 2010)

entrepreneurs in dealing with bureaucracy; second, the ratio of non-tax levies 

on enterprises (including illegal fees, apportion and fine from local 

government) as a percentage of sales. A formula similar to that for the 

Banking Index is used to standardize the measure. 

Patent Dummy Dummy variable equals 1 if the companies hold any patents before the current 

year. Patent could be innovation patent, utility model patent or design patent. 

SIPO 

According to the companies’ name, we hand collect the data of this variable 

from SIPO patent search website. 

Sales Growth Total sales growth rate. The value of this variable, when sales in the previous 

year is zero and the ratio is unbounded, is set equal to the maximum value in 

MOST 

the sample. 

Intangible to Total Intangible asset divided by total assets. 

MOST 

Assets Ratio 

ROA Net profit divided by total assets. MOST 

Leverage Book debt divided by total assets. MOST 

New Debt/Assets Ratio of net increase of debt in a given year to total assets at the beginning of 

the year. 

MOST 

New External Ratio of net increase of external equity in a given year to total assets at the 

Equity /Assets beginning of the year. Net increase of external equity is defined as the change 

MOST 

in book equity minus the change in retained earnings. 

New Internal Denotes the ratio of net increase in retain earnings in a given year to total 

Financing /Assets assets at the beginning of the year. 

MOST 

Innovation patents 

The number of Innovation patents created by the company in a given year. We 

hand collect the data for 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 calculated from the formula: k i,t = 

(1-δ) k i,t-1 +r i,t , where r i,t is the end-of-period stock of R&D capital and k i,t is 

MOST 

the R&D expenditure during the year t in thousand RMB. 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. 

Log(Assets) Natural logarithm of total assets in thousand RMB. MOST 

Log(Firm Age) Natural logarithm of firm age MOST 

Industry Dummies 21 industry dummies have been included in all equations in Tables 2 to 8. The 

classification of industry is from the Industry Classification Standard of CSRC 

MOST 

(China Securities Regulatory Commission). There are 13 major categories of 

industry according to Industry Classification Standard of CSRC, and the 

manufacturing industry is further divided into 10 sub-categories. An industry 

dummy takes the value 1 if the firm’s main activity is in that industry, and zero 

otherwise. 

47

Does Enforcement of Intellectual Property Rights Matter in China ...

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